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Genetic Programming vs Algorithms?

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  • John
    Hello everyone, I m very new to genetic algorithms. Actually I only started reading about them last week, though I m thoroughly intrigued. I ve coded up and
    Message 1 of 30 , Dec 1, 2003
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      Hello everyone,
      I'm very new to genetic algorithms. Actually I only started reading
      about them last week, though I'm thoroughly intrigued. I've coded up
      and played around with some examples I found in a book. But as I
      learn more and more, I've come up with several questions:

      1. What is the difference between genetic algorithms and genetic
      programming? They sound the same, but I'm not quite sure.
      2. For someone who knows the very basics and has done some
      coding with genetic algorithms, what book would be a good next step
      to get intermediate info in this area?
      3. Though I find this area very interesting, I'm having problems
      trying to find relevant applications with genetic algorithms. Now
      before everyone spasms me with examples, let me explain. I not a
      grad student or in any hard core engineer industry, I'm a developer
      in the retail industry and I write business software. Can someone
      give me examples or point me to a resource that shows how genetic
      algorithms can be applied to this industry?

      Thanks!
      John C
    • Martin Sewell
      ... Genetic programming is genetic algorithms applied to programs. ... See http://surf.de.uu.net/encore/www/Q10_6.htm ... Like most researchers in this area,
      Message 2 of 30 , Dec 2, 2003
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        At 20:16 01/12/2003 +0000, John wrote:
        >Hello everyone,
        >I'm very new to genetic algorithms. Actually I only started reading
        >about them last week, though I'm thoroughly intrigued. I've coded up
        >and played around with some examples I found in a book. But as I
        >learn more and more, I've come up with several questions:
        >
        >1. What is the difference between genetic algorithms and genetic
        >programming? They sound the same, but I'm not quite sure.

        Genetic programming is genetic algorithms applied to programs.

        >2. For someone who knows the very basics and has done some
        >coding with genetic algorithms, what book would be a good next step
        >to get intermediate info in this area?

        See http://surf.de.uu.net/encore/www/Q10_6.htm

        >3. Though I find this area very interesting, I'm having problems
        >trying to find relevant applications with genetic algorithms. Now
        >before everyone spasms me with examples, let me explain. I not a
        >grad student or in any hard core engineer industry, I'm a developer
        >in the retail industry and I write business software. Can someone
        >give me examples or point me to a resource that shows how genetic
        >algorithms can be applied to this industry?

        Like most researchers in this area, you're guilty of putting the technique
        cart before the issue horse; what do you want to optimize?

        Regards

        Martin
      • Sean Luke
        ... Perhaps a better rendition would be: Genetic programming is evolutionary computation applied to programs. Although I personally think that the following
        Message 3 of 30 , Dec 2, 2003
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          On Tuesday, December 2, 2003, at 05:30 AM, Martin Sewell wrote:

          > Genetic programming is genetic algorithms applied to programs.

          Perhaps a better rendition would be:

          Genetic programming is evolutionary computation applied to programs.

          Although I personally think that the following definition is better:

          Genetic programming is a community of researchers interested in
          applying evolutionary computation to the discovery of solutions in the
          form of computational devices.

          Sean
        • Mike Eggleston
          Hi Sean! ... Does the more robust definition also hold true for the automatic design of electronic circuts (or ants, etc)? Mike
          Message 4 of 30 , Dec 2, 2003
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            Hi Sean!

            On Tue, 02 Dec 2003, Sean Luke wrote:

            > On Tuesday, December 2, 2003, at 05:30 AM, Martin Sewell wrote:
            >
            > > Genetic programming is genetic algorithms applied to programs.
            >
            > Perhaps a better rendition would be:
            >
            > Genetic programming is evolutionary computation applied to programs.
            >
            > Although I personally think that the following definition is better:
            >
            > Genetic programming is a community of researchers interested in
            > applying evolutionary computation to the discovery of solutions in the
            > form of computational devices.
            >
            > Sean

            Does the more robust definition also hold true for
            the automatic design of electronic circuts (or ants, etc)?

            Mike
          • Una-May O'Reilly
            I prefer to say that GP is evolutionary algorithms that use executable structures (i.e. anything that can be executed, interpreted or (in the functional
            Message 5 of 30 , Dec 2, 2003
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              I prefer to say that GP is evolutionary algorithms that use
              executable structures (i.e. anything that can be executed, interpreted
              or (in the functional programming sense) evaluated). The word program is
              means a lot of different things and does not include graphs, analog
              circuits or other such representations some GP systems work with. Program
              also sometimes has a connotation of a particular syntax (sometimes
              specified by a BNF etc) that is not always what our GP systems work with.
              una-may


              On Tue, 2 Dec 2003,
              Sean Luke wrote:

              > On Tuesday, December 2, 2003, at 05:30 AM, Martin Sewell wrote:
              >
              > > Genetic programming is genetic algorithms applied to programs.
              >
              > Perhaps a better rendition would be:
              >
              > Genetic programming is evolutionary computation applied to programs.
              >
              > Although I personally think that the following definition is better:
              >
              > Genetic programming is a community of researchers interested in
              > applying evolutionary computation to the discovery of solutions in the
              > form of computational devices.
              >
              > Sean
              >
              >
              >
              > To unsubscribe from this group, send an email to:
              > genetic_programming-unsubscribe@yahoogroups.com
              >
              >
              >
              > Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
              >
              >
            • Korneel Duyvesteyn
              Hi, ... Isn t genetic programming simply Genetic Algorithms v2.0 , so GP is just GA but then uses program or structure instead of bit string (or object
              Message 6 of 30 , Dec 2, 2003
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                Hi,

                On Tuesday, December 2, 2003, at 05:30  AM, Martin Sewell wrote:

                > Genetic programming is genetic algorithms applied to programs.

                Perhaps a better rendition would be:

                      Genetic programming is evolutionary computation applied to programs.

                Although I personally think that the following definition is better:

                      Genetic programming is a community of researchers interested in
                applying evolutionary computation to the discovery of solutions in the
                form of computational devices.

                Sean

                Isn't genetic programming simply "Genetic Algorithms v2.0", so GP is just GA but then uses "program" or "structure" instead of "bit string" (or "object string" in case of ES)

                Does anyone know a problem which GA can solve but GP cannot? (in a reasonable way..)

                Korneel.

              • Tim Gordon
                Intrinsically evaluated circuits can exhibit complex dynamical interactions with a real physical environment. It may sometimes be useful to consider these
                Message 7 of 30 , Dec 2, 2003
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                  Intrinsically evaluated circuits can exhibit complex dynamical interactions
                  with a real physical environment. It may sometimes be useful to consider
                  these kinds of systems in a non-computational framework (See Inman Harvey,
                  "Cognition is Not Computation; Evolution is Not Optimisation", Proc. of 7th
                  Int. Conf. on Artificial Neural Networks, Lausanne, Switzerland, October
                  8-10, 1997.) and Sean's definition would not include these.

                  But in my opinion the question of whether EHW subsumes GP, GP subsumes GA
                  etc. etc. is moot and you should use the definition that best clarifies the
                  point you are trying to make.

                  -----Original Message-----
                  From: Mike Eggleston [mailto:mikee@...]
                  Sent: 02 December 2003 18:38
                  To: genetic_programming@yahoogroups.com
                  Subject: Re: [GP] Genetic Programming vs Algorithms?


                  Hi Sean!

                  On Tue, 02 Dec 2003, Sean Luke wrote:

                  > On Tuesday, December 2, 2003, at 05:30 AM, Martin Sewell wrote:
                  >
                  > > Genetic programming is genetic algorithms applied to programs.
                  >
                  > Perhaps a better rendition would be:
                  >
                  > Genetic programming is evolutionary computation applied to programs.
                  >
                  > Although I personally think that the following definition is better:
                  >
                  > Genetic programming is a community of researchers interested in
                  > applying evolutionary computation to the discovery of solutions in the
                  > form of computational devices.
                  >
                  > Sean

                  Does the more robust definition also hold true for
                  the automatic design of electronic circuts (or ants, etc)?

                  Mike


                  To unsubscribe from this group, send an email to:
                  genetic_programming-unsubscribe@yahoogroups.com



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                • Candida Ferreira
                  ... GAs are good at parameter optimization, a task GP cannot handle, although GEP, more related to GP than GAs, can handle this kind of problem very well
                  Message 8 of 30 , Dec 2, 2003
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                    Korneel Duyvesteyn wrote:

                    > Does anyone know a problem which GA can solve but GP cannot? (in a
                    > reasonable way..)

                    GAs are good at parameter optimization, a task GP cannot handle, although GEP, more related to GP than GAs, can handle this kind of problem very well thanks to its multigenic nature (see my book Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence for details).

                    Cheers,
                    Candida Ferreira

                    -----------------------------------------------------------
                    Candida Ferreira, Ph.D.
                    Chief Scientist, Gepsoft
                    73 Elmtree Drive
                    Bristol BS13 8NA, UK
                    ph: +44 (0) 117 330 9272
                    http://www.gepsoft.com/gepsoft
                    http://www.gene-expression-programming.com/author.asp
                    -----------------------------------------------------------
                  • Tom Osborn UTS
                    (1) For me, GA is search in a parameter space (using evolutionary operators in the search). GP as a search in a space of composition of structures using the
                    Message 9 of 30 , Dec 2, 2003
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                      (1) For me, GA is search in a parameter space (using evolutionary operators
                      in the search).

                      GP as a search in a space of composition of structures using the same
                      kinds of evolutionary operators.

                      The structures may be expression trees, electronic circuits, traversable
                      graphs, etc. Pretty much anything that can be composed and still have
                      a behaviour.

                      (2) My suggestions for reading would be to read an much as possible.
                      Koza's volumes and Banzaf's "GP and Intro" would be a good start.
                      Become a friend of http://citeseer.nj.nec.com/cs.

                      (3) For examples, Koza's books are groaning with examples...

                      Tom.

                      ----- Original Message -----
                      From: "John" <rainerclimbing@...>
                      To: <genetic_programming@yahoogroups.com>
                      Sent: Tuesday, December 02, 2003 7:16 AM
                      Subject: [GP] Genetic Programming vs Algorithms?


                      > Hello everyone,
                      > I'm very new to genetic algorithms. Actually I only started reading
                      > about them last week, though I'm thoroughly intrigued. I've coded up
                      > and played around with some examples I found in a book. But as I
                      > learn more and more, I've come up with several questions:
                      >
                      > 1. What is the difference between genetic algorithms and genetic
                      > programming? They sound the same, but I'm not quite sure.
                      > 2. For someone who knows the very basics and has done some
                      > coding with genetic algorithms, what book would be a good next step
                      > to get intermediate info in this area?
                      > 3. Though I find this area very interesting, I'm having problems
                      > trying to find relevant applications with genetic algorithms. Now
                      > before everyone spasms me with examples, let me explain. I not a
                      > grad student or in any hard core engineer industry, I'm a developer
                      > in the retail industry and I write business software. Can someone
                      > give me examples or point me to a resource that shows how genetic
                      > algorithms can be applied to this industry?
                      >
                      > Thanks!
                      > John C
                      >
                      >
                      >
                      >
                      > To unsubscribe from this group, send an email to:
                      > genetic_programming-unsubscribe@yahoogroups.com
                      >
                      >
                      >
                      > Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
                      >
                      >
                      >
                    • Sean Luke
                      ... Sure, I think so. Note that the definition also probably applies to learning classifier systems, and to CAs evolved by GA etc. Una-May s term executable
                      Message 10 of 30 , Dec 2, 2003
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                        On Tuesday, December 2, 2003, at 01:38 PM, Mike Eggleston wrote:

                        > On Tue, 02 Dec 2003, Sean Luke wrote:
                        >
                        >> Although I personally think that the following definition is better:
                        >>
                        >> Genetic programming is a community of researchers interested in
                        >> applying evolutionary computation to the discovery of solutions in the
                        >> form of computational devices.
                        >
                        > Does the more robust definition also hold true for
                        > the automatic design of electronic circuts (or ants, etc)?

                        Sure, I think so.

                        Note that the definition also probably applies to learning classifier
                        systems, and to CAs evolved by GA etc.

                        Una-May's term "executable structures" might also work instead of
                        "computational devices".

                        Sean
                      • Steffen Christensen and Lisa Jayne
                        ... programs. ... From a computational point of view, and using GP = GA using trees as representation rather than bit strings (the obvious definition),
                        Message 11 of 30 , Dec 3, 2003
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                          > Date: Tue, 02 Dec 2003 21:19:48 +0100
                          > From: Korneel Duyvesteyn <189770cd@...>
                          > Subject: Re: Genetic Programming vs Algorithms?

                          >>On Tuesday, December 2, 2003, at 05:30 AM, Martin Sewell wrote:
                          >>
                          >> > Genetic programming is genetic algorithms applied to programs.
                          >>
                          >>Perhaps a better rendition would be:
                          >>
                          >> Genetic programming is evolutionary computation applied to
                          programs.
                          >>
                          >>Although I personally think that the following definition is better:
                          >>
                          >> Genetic programming is a community of researchers interested in
                          >>applying evolutionary computation to the discovery of solutions in the
                          >>form of computational devices.
                          >>
                          >>Sean

                          > Isn't genetic programming simply "Genetic Algorithms v2.0", so GP is just
                          > GA but then uses "program" or "structure" instead of "bit string" (or
                          > "object string" in case of ES)

                          > Does anyone know a problem which GA can solve but GP cannot? (in a
                          > reasonable way..)

                          > Korneel.

                          From a computational point of view, and using "GP = GA using trees as
                          representation rather than bit strings" (the "obvious" definition), there
                          can't be any such problem, since Dr. Poli gave us this nice onto mapping
                          from GA bit-strings to GP function trees. Basically, make two nodes types
                          of arity 1, labelled "0" and "1", and one node of arity 0, labelled
                          "end-of-string". Make an initial population of nodes of size B + 1 (B being
                          the number of bits in your GA bit string), and use a structure-preserving
                          crossover, which only exchanges isomorphic subtrees. Boom! GA encoded in
                          GP.

                          Of course, if you try this with lil-gp or ECJ, you'll see a slowdown of (I'm
                          guessing) 100x in the crossover operators, since bits are such nice things
                          to operate on in a binary computer. But that's more due to the efficiency
                          of bit representations generally on our PCs than any intrinsic slowness of
                          GP.

                          Finally, I'd like to point out that GP is used in two senses, really, which
                          were pointed out by others in this group. GP is used to mean "tree-based
                          evolutionary computing"; and it is also used to mean "evolutionary computing
                          using executable problem structures. I prefer to think of the second
                          definition as "automated algorithm discovery", but that's just the way I
                          conceptualize it.

                          My $0.02

                          Steffen Christensen
                          Ph.D. Candidate, Evolutionary Computation (Computer Science)
                          Carleton University, Ottawa, Canada
                          Email: idyll at rogers.com
                        • dennis petkiewicz
                          Hello everyone... What would it take for GP to go mainstream? To be as easy for the general population or select knowledge workers to use as Excel or
                          Message 12 of 30 , Dec 3, 2003
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                            Hello everyone...

                            What would it take for GP to go mainstream? To be as
                            easy for the general population or select knowledge
                            workers to use as Excel or something similar to solve problems?

                            __________________________________
                            Do you Yahoo!?
                            Free Pop-Up Blocker - Get it now
                            http://companion.yahoo.com/
                          • Candida Ferreira
                            ... You are confusing plain parameter optimization with function optimization. The parameter optimization alluded to in my previous email is the one involving
                            Message 13 of 30 , Dec 3, 2003
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                              John Koza wrote:

                              > The posting below asserts
                              > "GAs are good at parameter optimization, a task GP cannot handle, ..."
                              >
                              > This is not true. There are hundreds of examples (both in my books and
                              > elsewhere) from dozens of different areas where parameter optimization takes
                              > place inside a GP run. To take just a few examples, there is parameter
                              > optimization that takes places (simultaneously with topology creation) in
                              > problems involving circuits, controllers, antennas, chemical networks,
                              > etc. --- to saynothing of parameter optimization that takes places in just
                              > plain old mathematical expressions in symbolic regression runs.

                              You are confusing plain parameter optimization with function optimization. The parameter optimization alluded to in my previous email is the one involving minimum or maximum seeking, a task handled particularly successfully by GAs and now, I might add, by GEP.

                              Cheers,
                              Candida Ferreira

                              -----------------------------------------------------------
                              Candida Ferreira, Ph.D.
                              Chief Scientist, Gepsoft
                              73 Elmtree Drive
                              Bristol BS13 8NA, UK
                              ph: +44 (0) 117 330 9272
                              http://www.gepsoft.com/gepsoft
                              http://www.gene-expression-programming.com/author.asp
                              -----------------------------------------------------------
                            • C. Setzkorn
                              I dont get you here Candida. Lets say we evolve a polynomial using GP (e.g. for regression) then we clearly have to optimise the parameters of the polynomial.
                              Message 14 of 30 , Dec 3, 2003
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                                I dont get you here Candida. Lets say we evolve a polynomial using GP
                                (e.g. for regression) then we clearly have to optimise the parameters of
                                the polynomial. The good thing about GP is that it can easily drop terms
                                and does not have to evolve the corresponding parameters to zero.
                                Hence the search space can be restricted through structural
                                optimisation. One can also mutate terminals (parameters) by adding some
                                random noise as in (real valued) GAs.

                                Candida Ferreira wrote:

                                >John Koza wrote:
                                >
                                >
                                >
                                >>The posting below asserts
                                >>"GAs are good at parameter optimization, a task GP cannot handle, ..."
                                >>
                                >>This is not true. There are hundreds of examples (both in my books and
                                >>elsewhere) from dozens of different areas where parameter optimization takes
                                >>place inside a GP run. To take just a few examples, there is parameter
                                >>optimization that takes places (simultaneously with topology creation) in
                                >>problems involving circuits, controllers, antennas, chemical networks,
                                >>etc. --- to saynothing of parameter optimization that takes places in just
                                >>plain old mathematical expressions in symbolic regression runs.
                                >>
                                >>
                                >
                                >You are confusing plain parameter optimization with function optimization. The parameter optimization alluded to in my previous email is the one involving minimum or maximum seeking, a task handled particularly successfully by GAs and now, I might add, by GEP.
                                >
                                >Cheers,
                                >Candida Ferreira
                                >
                                >-----------------------------------------------------------
                                >Candida Ferreira, Ph.D.
                                >Chief Scientist, Gepsoft
                                >73 Elmtree Drive
                                >Bristol BS13 8NA, UK
                                >ph: +44 (0) 117 330 9272
                                >http://www.gepsoft.com/gepsoft
                                >http://www.gene-expression-programming.com/author.asp
                                >-----------------------------------------------------------
                                >
                                >
                                >
                                >
                                >To unsubscribe from this group, send an email to:
                                >genetic_programming-unsubscribe@yahoogroups.com
                                >
                                >
                                >
                                >Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
                                >
                                >
                                >
                                >
                              • John Koza
                                Frankly, I don t think I m the one who s confused. Have a nice day. John Koza ... From: Candida Ferreira [mailto:candidaf@gene-expression-programming.com]
                                Message 15 of 30 , Dec 3, 2003
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                                  Frankly, I don't think I'm the one who's confused.

                                  Have a nice day.

                                  John Koza


                                  -----Original Message-----
                                  From: Candida Ferreira [mailto:candidaf@...]
                                  Sent: Wednesday, December 03, 2003 12:01 PM
                                  To: genetic_programming@yahoogroups.com
                                  Subject: Re: [GP] Genetic Programming vs Algorithms?



                                  John Koza wrote:

                                  > The posting below asserts
                                  > "GAs are good at parameter optimization, a task GP cannot handle, ..."
                                  >
                                  > This is not true. There are hundreds of examples (both in my books and
                                  > elsewhere) from dozens of different areas where parameter optimization
                                  takes
                                  > place inside a GP run. To take just a few examples, there is parameter
                                  > optimization that takes places (simultaneously with topology creation) in
                                  > problems involving circuits, controllers, antennas, chemical networks,
                                  > etc. --- to saynothing of parameter optimization that takes places in just
                                  > plain old mathematical expressions in symbolic regression runs.

                                  You are confusing plain parameter optimization with function optimization.
                                  The parameter optimization alluded to in my previous email is the one
                                  involving minimum or maximum seeking, a task handled particularly
                                  successfully by GAs and now, I might add, by GEP.

                                  Cheers,
                                  Candida Ferreira

                                  -----------------------------------------------------------
                                  Candida Ferreira, Ph.D.
                                  Chief Scientist, Gepsoft
                                  73 Elmtree Drive
                                  Bristol BS13 8NA, UK
                                  ph: +44 (0) 117 330 9272
                                  http://www.gepsoft.com/gepsoft
                                  http://www.gene-expression-programming.com/author.asp
                                  -----------------------------------------------------------




                                  To unsubscribe from this group, send an email to:
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                                • Candida Ferreira
                                  ... Evolving polynomials for symbolic regression is a form of function optimization, not plain parameter optimization (also a form of function optimization).
                                  Message 16 of 30 , Dec 3, 2003
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                                    C. Setzkorn wrote:


                                    > I dont get you here Candida. Lets say we evolve a polynomial using GP
                                    > (e.g. for regression) then we clearly have to optimise the parameters of
                                    > the polynomial. The good thing about GP is that it can easily drop terms
                                    > and does not have to evolve the corresponding parameters to zero.
                                    > Hence the search space can be restricted through structural
                                    > optimisation. One can also mutate terminals (parameters) by adding some
                                    > random noise as in (real valued) GAs.
                                    >
                                    > Candida Ferreira wrote:
                                    >
                                    > >John Koza wrote:
                                    > >
                                    > >
                                    > >
                                    > >>The posting below asserts
                                    > >>"GAs are good at parameter optimization, a task GP cannot handle, ..."
                                    > >>
                                    > >>This is not true. There are hundreds of examples (both in my books and
                                    > >>elsewhere) from dozens of different areas where parameter optimization takes
                                    > >>place inside a GP run. To take just a few examples, there is parameter
                                    > >>optimization that takes places (simultaneously with topology creation) in
                                    > >>problems involving circuits, controllers, antennas, chemical networks,
                                    > >>etc. --- to saynothing of parameter optimization that takes places in just
                                    > >>plain old mathematical expressions in symbolic regression runs.
                                    > >>
                                    > >>
                                    > >
                                    > >You are confusing plain parameter optimization with function optimization. The parameter optimization alluded to in my previous email is the one involving minimum or maximum seeking, a task handled particularly successfully by GAs and now, I might add, by GEP.
                                    > >

                                    Evolving polynomials for symbolic regression is a form of function optimization, not plain parameter optimization (also a form of function optimization). In parameter optimization you evolve the coefficients of the parameters of the function being optimized so that the output of the function is the smallest possible.

                                    Candida Ferreira

                                    -----------------------------------------------------------
                                    Candida Ferreira, Ph.D.
                                    Chief Scientist, Gepsoft
                                    73 Elmtree Drive
                                    Bristol BS13 8NA, UK
                                    ph: +44 (0) 117 330 9272
                                    http://www.gepsoft.com/gepsoft
                                    http://www.gene-expression-programming.com/author.asp
                                    -----------------------------------------------------------
                                  • voyantix
                                    Greetings, It is not difficult to demonstrate that the statement: GAs are good at parameter optimization, a task GP cannot handle, ... is false. If using any
                                    Message 17 of 30 , Dec 3, 2003
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                                      Greetings,

                                      It is not difficult to demonstrate that the
                                      statement:

                                      "GAs are good at parameter optimization, a task GP cannot handle, ..."

                                      is false.

                                      If using any general GP system we could emulate GA then
                                      that would be sufficient to prove the falsehood of the claim.

                                      For instance, for binary-string GA:

                                      Choose the function set: FONE, FZERO each with one argument.
                                      The functions return the eval of their argument concatenated
                                      with their own (1 and 0 respt).

                                      Choose the terminal set: TONE, TZERO which return 1 or 0 respt.

                                      Build/create trees with the FULL method, up to depth N (equivalent
                                      to the lenght of the GA strings). The fitness eval function should
                                      interpret each tree in exactly the same way as traditional GA would
                                      process the strings.

                                      With the standard GP genetic operators, this scheme emulates
                                      binary string GA. This scheme can be easily generalized to any
                                      kind of GA representation, ie, non-binary.

                                      Regards,

                                      David Anisman


                                      --- In genetic_programming@yahoogroups.com, "Candida Ferreira"
                                      <candidaf@g...> wrote:
                                      >
                                      > John Koza wrote:
                                      >
                                      > > The posting below asserts
                                      > > "GAs are good at parameter optimization, a task GP cannot handle,
                                      ..."
                                      > >
                                      > > This is not true. There are hundreds of examples (both in my
                                      books and
                                      > > elsewhere) from dozens of different areas where parameter
                                      optimization takes
                                      > > place inside a GP run. To take just a few examples, there is
                                      parameter
                                      > > optimization that takes places (simultaneously with topology
                                      creation) in
                                      > > problems involving circuits, controllers, antennas, chemical
                                      networks,
                                      > > etc. --- to saynothing of parameter optimization that takes places
                                      in just
                                      > > plain old math

                                      ematical expressions in symbolic regression runs.
                                      >
                                      > You are confusing plain parameter optimization with function
                                      optimization. The parameter optimization alluded to in my previous
                                      email is the one involving minimum or maximum seeking, a task handled
                                      particularly successfully by GAs and now, I might add, by GEP.
                                      >
                                      > Cheers,
                                      > Candida Ferreira
                                      >
                                      > -----------------------------------------------------------
                                      > Candida Ferreira, Ph.D.
                                      > Chief Scientist, Gepsoft
                                      > 73 Elmtree Drive
                                      > Bristol BS13 8NA, UK
                                      > ph: +44 (0) 117 330 9272
                                      > http://www.gepsoft.com/gepsoft
                                      > http://www.gene-expression-programming.com/author.asp
                                      > -----------------------------------------------------------
                                    • C. Setzkorn
                                      ... In regression you optimize the parameters of your model (e.g. the parameters of the polynomial if you have chosen a polynomial as your model) Good fit of
                                      Message 18 of 30 , Dec 3, 2003
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                                        >
                                        >
                                        >Evolving polynomials for symbolic regression is a form of function optimization, not plain parameter optimization (also a form of function optimization). In parameter optimization you evolve the coefficients of the parameters of the function being optimized so that the output of the function is the smallest possible.
                                        >
                                        In regression you optimize the parameters of your model (e.g. the
                                        parameters of the polynomial if you have chosen a polynomial as your model)

                                        Good fit of the model can be measured by the MSE or the absolute error.

                                        I don't see a difference ... sorry.

                                        Chris

                                        >
                                        >Candida Ferreira
                                        >
                                        >-----------------------------------------------------------
                                        >Candida Ferreira, Ph.D.
                                        >Chief Scientist, Gepsoft
                                        >73 Elmtree Drive
                                        >Bristol BS13 8NA, UK
                                        >ph: +44 (0) 117 330 9272
                                        >http://www.gepsoft.com/gepsoft
                                        >http://www.gene-expression-programming.com/author.asp
                                        >-----------------------------------------------------------
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                                      • Candida Ferreira
                                        I m sure that with a little ingenuity you will be able to emulate even Microsoft Excel in GP. Candida Ferreira ... Candida Ferreira, Ph.D. Chief Scientist,
                                        Message 19 of 30 , Dec 3, 2003
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                                          I'm sure that with a little ingenuity you will be able to emulate even Microsoft Excel in GP.

                                          Candida Ferreira

                                          -----------------------------------------------------------
                                          Candida Ferreira, Ph.D.
                                          Chief Scientist, Gepsoft
                                          73 Elmtree Drive
                                          Bristol BS13 8NA, UK
                                          ph: +44 (0) 117 330 9272
                                          http://www.gepsoft.com/gepsoft
                                          http://www.gene-expression-programming.com/author.asp
                                          -----------------------------------------------------------

                                          ----- Original Message -----
                                          From: "voyantix" <voyantix@...>
                                          To: <genetic_programming@yahoogroups.com>
                                          Sent: Wednesday, December 03, 2003 9:29 PM
                                          Subject: Re: [GP] Genetic Programming vs Algorithms?


                                          > Greetings,
                                          >
                                          > It is not difficult to demonstrate that the
                                          > statement:
                                          >
                                          > "GAs are good at parameter optimization, a task GP cannot handle, ..."
                                          >
                                          > is false.
                                          >
                                          > If using any general GP system we could emulate GA then
                                          > that would be sufficient to prove the falsehood of the claim.
                                          >
                                          > For instance, for binary-string GA:
                                          >
                                          > Choose the function set: FONE, FZERO each with one argument.
                                          > The functions return the eval of their argument concatenated
                                          > with their own (1 and 0 respt).
                                          >
                                          > Choose the terminal set: TONE, TZERO which return 1 or 0 respt.
                                          >
                                          > Build/create trees with the FULL method, up to depth N (equivalent
                                          > to the lenght of the GA strings). The fitness eval function should
                                          > interpret each tree in exactly the same way as traditional GA would
                                          > process the strings.
                                          >
                                          > With the standard GP genetic operators, this scheme emulates
                                          > binary string GA. This scheme can be easily generalized to any
                                          > kind of GA representation, ie, non-binary.
                                          >
                                          > Regards,
                                          >
                                          > David Anisman
                                          >
                                          >
                                          > --- In genetic_programming@yahoogroups.com, "Candida Ferreira"
                                          > <candidaf@g...> wrote:
                                          > >
                                          > > John Koza wrote:
                                          > >
                                          > > > The posting below asserts
                                          > > > "GAs are good at parameter optimization, a task GP cannot handle,
                                          > ..."
                                          > > >
                                          > > > This is not true. There are hundreds of examples (both in my
                                          > books and
                                          > > > elsewhere) from dozens of different areas where parameter
                                          > optimization takes
                                          > > > place inside a GP run. To take just a few examples, there is
                                          > parameter
                                          > > > optimization that takes places (simultaneously with topology
                                          > creation) in
                                          > > > problems involving circuits, controllers, antennas, chemical
                                          > networks,
                                          > > > etc. --- to saynothing of parameter optimization that takes places
                                          > in just
                                          > > > plain old math
                                          >
                                          > ematical expressions in symbolic regression runs.
                                          > >
                                          > > You are confusing plain parameter optimization with function
                                          > optimization. The parameter optimization alluded to in my previous
                                          > email is the one involving minimum or maximum seeking, a task handled
                                          > particularly successfully by GAs and now, I might add, by GEP.
                                          > >
                                          > > Cheers,
                                          > > Candida Ferreira
                                          > >
                                          > > -----------------------------------------------------------
                                          > > Candida Ferreira, Ph.D.
                                          > > Chief Scientist, Gepsoft
                                          > > 73 Elmtree Drive
                                          > > Bristol BS13 8NA, UK
                                          > > ph: +44 (0) 117 330 9272
                                          > > http://www.gepsoft.com/gepsoft
                                          > > http://www.gene-expression-programming.com/author.asp
                                          > > -----------------------------------------------------------
                                          >
                                          >
                                          >
                                          > To unsubscribe from this group, send an email to:
                                          > genetic_programming-unsubscribe@yahoogroups.com
                                          >
                                          >
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                                          >
                                          >
                                          >
                                        • Mariusz Nowostawski
                                          Hi David, Your proof has a mistake and is invalid. ... This is correct. Hovewer, to emulate GA in GP in the context of this discussion is a bit
                                          Message 20 of 30 , Dec 3, 2003
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                                            Hi David,


                                            Your "proof" has a "mistake" and is invalid.


                                            voyantix wrote:
                                            > It is not difficult to demonstrate that the statement:
                                            >
                                            > "GAs are good at parameter optimization, a task GP cannot handle, ..."
                                            >
                                            > is false.
                                            >
                                            > If using any general GP system we could emulate GA then
                                            > that would be sufficient to prove the falsehood of the claim.

                                            This is correct. Hovewer, to "emulate" GA in GP in the context of this
                                            discussion is a bit 'tricky'...

                                            Before we start, we need to agree with one simple assumption (axiom so
                                            to speak), to make the whole discussion worthwhile, and we have to agree
                                            with one terminological aspect of TREES vs STRINGS distinction:


                                            Assumption 1:

                                            GA == representation of the genome is a STRING. This STRING after
                                            'evaluation' represents a point in a (multidimensional) fitness
                                            landscape space.

                                            GP == representation of the genome is a TREE. This TREE after
                                            'evaluation' represents a point in a (multidimensional) programs space.
                                            The particular program's output represents a point in a
                                            (multidimensional) fitness landscape space.


                                            Assumption 2:

                                            STRING != TREE

                                            (in other words, if something is a STRING, it is not a TREE. If
                                            something is a TREE, it is by definiton not a STRING. Therefore, trees
                                            consisting only of one branch, are (according to Assumption 2) strings,
                                            and are not trees).


                                            If we assume 1 without assuming 2 we cannot continue. It would be like
                                            postulating that GP *IS* a special case of GA (which of course is true
                                            in some sense, but this case is least interesting here, and we would
                                            have nothing to talk about).

                                            It seems like the whole point of GP vs. GA disscussion is more or less
                                            like this "are tree-based representations 'different' to string-based
                                            representations, and if so, for what kind of problems one is superior to
                                            the other." If TREE-based representation *IS* superior in some
                                            problems to pure STRING-based representations (or other way round), from
                                            NFL theorem we *know* it must be inferior in some other problems (or
                                            other way round). If they both are just isomorphic to one another, than
                                            there is no difference to do GA or to do GP.


                                            One can map STRING to a tree, as STRING is actually a special kind of
                                            tree, and one can map TREE to a STRING, e.g. by using reversed polish
                                            notation. But mapping one representation into the other does not
                                            automatically mean the evolutionary dynamics is isomorphic in both
                                            cases. This is the whole point - the dynamics of the evolutionary
                                            search process - not the mappings of representations.

                                            Some claim the evolutionary search is isomorphic, some claim it is not.

                                            Your proof does not answer the question one way or the other unfortunatelly.

                                            > For instance, for binary-string GA:
                                            >
                                            > Choose the function set: FONE, FZERO each with one argument.
                                            > The functions return the eval of their argument concatenated
                                            > with their own (1 and 0 respt).
                                            >
                                            > Choose the terminal set: TONE, TZERO which return 1 or 0 respt.

                                            So far so good, but.... you have actually defined a representation
                                            method which produces STRINGS, therefore, it actually *IS* GA, not GP
                                            (see assumptions above).

                                            > Build/create trees with the FULL method, up to depth N (equivalent

                                            You actually building STRINGS, not TREES (see assumptions above).

                                            > to the lenght of the GA strings). The fitness eval function should
                                            > interpret each tree in exactly the same way as traditional GA would
                                            > process the strings.

                                            This actually *IS* GA, not GP (see assumptions above).

                                            > With the standard GP genetic operators, this scheme emulates
                                            > binary string GA. This scheme can be easily generalized to any

                                            This scheme, based on STRING representation, does not emulate GA in GP,
                                            it actually *is* GA.

                                            > kind of GA representation, ie, non-binary.

                                            > Regards,
                                            >
                                            > David Anisman


                                            --
                                            best regards
                                            Mariusz Nowostawski
                                          • Korneel Duyvesteyn
                                            Hi, ... What Candida means is that many things can be emulated but are simply not designed for it. If you solve a problem with GP with a function set
                                            Message 21 of 30 , Dec 3, 2003
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                                              Hi,

                                              "Candida Ferreira" <candidaf@...> writes:

                                              > I'm sure that with a little ingenuity you will be able to emulate even
                                              > Microsoft Excel in GP.

                                              Good luck trying to define a fitness-function that will select offspring
                                              for "Excel-like" behavior !!!

                                              What Candida means is that many things can be emulated but are simply not designed for it.
                                              If you solve a problem with GP with a function set containing functions with 2 arguments using the Full method
                                              without mutation, you can also use GA to solve this problem in a very sophisticated way (converting a binary tree into bits),
                                              but GA  is just not the best approach to it.

                                              And what Candida means (I think) by the difference between parameter and function optimization is for example, when GP
                                              is used to generate the equation y = 0.75x + 0.6x^2
                                              GP will probably easily find y = ax + bx^2 but will probably not find values for 'a' and 'b' as optimal as GA would, or am I mistaken?

                                              Regards, Korneel.



                                              -- Gordon D. Pusch  

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                                            • Gordon D. Pusch
                                              ... Good luck trying to define a fitness-function that will select offspring for Excel-like behavior !!! -- Gordon D. Pusch perl -e $_ =
                                              Message 22 of 30 , Dec 3, 2003
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                                                "Candida Ferreira" <candidaf@...> writes:

                                                > I'm sure that with a little ingenuity you will be able to emulate even
                                                > Microsoft Excel in GP.

                                                Good luck trying to define a fitness-function that will select offspring
                                                for "Excel-like" behavior !!!


                                                -- Gordon D. Pusch

                                                perl -e '$_ = "gdpusch\@...\n"; s/NO\.//; s/SPAM\.//; print;'
                                              • Russ Abbott
                                                Although the discussion has drifted a bit from GP vs. algorithms, here is a problem that might illustrate the difference. For an AI class, I made up a
                                                Message 23 of 30 , Dec 3, 2003
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                                                  Although the discussion has drifted a bit from GP vs. algorithms, here is a problem that might illustrate the difference.
                                                   
                                                  For an AI class, I made up a two-player game. It is played on a network. Each side starts with N pieces (for some N) on the network nodes.  The players can move from one node to another by following the edges of the network,  (To make it more interesting, the players have separate graphs, although the sets of nodes in the two graphs overlap.)
                                                   
                                                  If a player lands on a node that contains pieces owned by the other player, the other player's pieces are removed from the game.  A player wins if he removes all of the other player's pieces or if the other player has no move because all his pieces are at dead-end node. 
                                                   
                                                  So this is something like checkers played on a pair of graphs with the following differences.
                                                  • Pieces are taken when you land on their square instead of jumping them.
                                                  • It is allowed to have multiple pieces on a square.
                                                  • There are no special pieces (like kings).
                                                  Since this was an AI class, the point was to illustrate minimax, which it does quite well.  It's amazing how difficult it is for a human to look ahead, especially on an unfamiliar board.  (The "board" consists of a pair of graphs, one for each player.)  But of course, a minimax player can look ahead very easily.
                                                   
                                                  Minimax is an algorithm. Although not trivial, it is not a very complex algorithm.  How would one attack this problem with GP?
                                                   
                                                  As a first approximation, one could fix the board and use GP to evolve a player for  that specific game.  
                                                   
                                                  I imagine it would be a lot more challenging to use GP to evolve a player for a game in which the board itself was part of the input.  Yet a graph is a data structure, and one could give the GP system a set of operators that let it move around on its input graphs. 
                                                   
                                                  Assuming that I'm right and that GP will not be successful at evolving a general player for this game, this suggests that GP is capable of evolving relatively simple functions but not very good at evolving algorithms.  For a further discussion of this point see: http://abbott.calstatela.edu/oogp/GuidedGP.doc.
                                                   
                                                   
                                                  For slightly more information about this game and the assignments associated with it, look at http://abbott.calstatela.edu/courses/cs_460.  This is assignments 9,10, and 11.  A minimax prolog player for the game is available at http://abbott.calstatela.edu/Courses/CS_460/Network%20Game/.
                                                   
                                                  To run the game, load prolog and then:
                                                   
                                                      ?-  testGame(<Player1>, [e, b], <Player2>, [c, b]).
                                                   
                                                  where <Player1> and <Player2> are one of human, random, or minimax.
                                                  There is a nice (free) prolog at: http://www.swi-prolog.org/.
                                                   
                                                  -- Russ Abbott
                                                   
                                                   
                                                • Gordon D. Pusch
                                                  ... Korneel: I think your sarcasm detector needs a tune-up. My remark was not meant to be taken particularly seriously... BTW, was anyone else in the group
                                                  Message 24 of 30 , Dec 3, 2003
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                                                    Korneel Duyvesteyn <189770cd@...> writes:

                                                    >> "Candida Ferreira" <candidaf@...> writes:
                                                    >>
                                                    >>> I'm sure that with a little ingenuity you will be able to emulate even
                                                    >>> Microsoft Excel in GP.
                                                    >>
                                                    >> Good luck trying to define a fitness-function that will select offspring
                                                    >> for "Excel-like" behavior !!!
                                                    >
                                                    > What Candida means is that many things can be emulated but are simply not
                                                    > designed for it.

                                                    Korneel: I think your sarcasm detector needs a tune-up. My remark was not
                                                    meant to be taken particularly seriously...

                                                    BTW, was anyone else in the group annoyed by that 44k base-64 encoded JPEG
                                                    advertisement that Yahoo appended to Korneel's followup ??? Yahoo's ads
                                                    were annoying enough when they were short bits of HTML, but when they start
                                                    shoving binary images down our throats, I wonder if it's once again time
                                                    for the group to look for Yet Another Home --- before Yahoo decides to allow
                                                    JavaScript ads that could potentially contain viruses or "spyware"... >:-(


                                                    -- Gordon D. Pusch

                                                    perl -e '$_ = "gdpusch\@...\n"; s/NO\.//; s/SPAM\.//; print;'
                                                  • Martin Sewell
                                                    ... GP is a subset of, and more expressive than, GAs, though GAs are likely to be more efficient for some classes of problems. GEP, in contrast, is overtly
                                                    Message 25 of 30 , Dec 3, 2003
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                                                      At 21:26 02/12/2003 +0000, Candida Ferreira wrote:

                                                      >Korneel Duyvesteyn wrote:
                                                      >
                                                      > > Does anyone know a problem which GA can solve but GP cannot? (in a
                                                      > > reasonable way..)
                                                      >
                                                      >GAs are good at parameter optimization, a task GP cannot handle, although
                                                      >GEP, more related to GP than GAs, can handle this kind of problem very
                                                      >well thanks to its multigenic nature (see my book <SNIP>).

                                                      GP is a subset of, and more expressive than, GAs, though GAs are likely to
                                                      be more efficient for some classes of problems. GEP, in contrast, is
                                                      overtly more commercial.

                                                      Regards

                                                      Martin

                                                      Disclaimer: <NFL>For any algorithm, any elevated performance over one class
                                                      of problems is exactly paid for in performance over another class.</NFL>
                                                    • Mariusz Nowostawski
                                                      Hi Martin, Martin Sewell wrote: [...] ... I think I can understand what you try to communicate, but the sentence above is unfortunatelly not consistent. First,
                                                      Message 26 of 30 , Dec 3, 2003
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                                                        Hi Martin,

                                                        Martin Sewell wrote:
                                                        [...]
                                                        > GP is a subset of, and more expressive than, GAs, though GAs are likely to
                                                        > be more efficient for some classes of problems.

                                                        I think I can understand what you try to communicate, but the sentence
                                                        above is unfortunatelly not consistent.

                                                        First, if "GP is a subset of GAs", then GP by definition cannot be "more
                                                        expressive than GAs" -- because GAs, by definition would be a superset
                                                        containing GP+other stuff, therefore GA could express all GP can express
                                                        plus more, which GP cannot express.

                                                        Second, if "GP is a subset of GAs" then it is not only "likely", but it
                                                        is actually a simple fact (by definition) that GAs are "more efficient
                                                        for some classes of problems" -- because everything GP can do GAs can do
                                                        as well, but not all GAs can do can be repeated by GP with the same
                                                        efficiency.

                                                        As I said before, any talk on the subject "GP vs. GA" must be preceded
                                                        by the agreement on some basic understanding of the words being used in
                                                        the discourse - just by shuffling the words and using them in different
                                                        context with different semantics will lead nowhere.

                                                        >
                                                        > Regards
                                                        > Martin


                                                        --
                                                        best regards
                                                        Mariusz
                                                      • Martin Sewell
                                                        ... The above sentence is false. :-) ... More expressive , not expresses more . ... The qualifier likely is used because there exists a GA which would have
                                                        Message 27 of 30 , Dec 3, 2003
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                                                          At 15:36 04/12/2003 +1300, Mariusz Nowostawski wrote:
                                                          >Hi Martin,
                                                          >
                                                          >Martin Sewell wrote:
                                                          >[...]
                                                          > > GP is a subset of, and more expressive than, GAs, though GAs are likely to
                                                          > > be more efficient for some classes of problems.
                                                          >
                                                          >I think I can understand what you try to communicate, but the sentence
                                                          >above is unfortunatelly not consistent.

                                                          The above sentence is false. :-)

                                                          >First, if "GP is a subset of GAs", then GP by definition cannot be "more
                                                          >expressive than GAs" -- because GAs, by definition would be a superset
                                                          >containing GP+other stuff, therefore GA could express all GP can express
                                                          >plus more, which GP cannot express.

                                                          "More expressive", not "expresses more".

                                                          >Second, if "GP is a subset of GAs" then it is not only "likely", but it
                                                          >is actually a simple fact (by definition) that GAs are "more efficient
                                                          >for some classes of problems" -- because everything GP can do GAs can do
                                                          >as well, but not all GAs can do can be repeated by GP with the same
                                                          >efficiency.

                                                          The qualifier "likely" is used because there exists a GA which would have
                                                          an equivalent GP, for which the statement would be false. I also included
                                                          a disclaimer: "<NFL>For any algorithm, any elevated performance over one
                                                          class of problems is exactly paid for in performance over another class.</NFL>"

                                                          >As I said before, any talk on the subject "GP vs. GA" must be preceded
                                                          >by the agreement on some basic understanding of the words being used in
                                                          >the discourse - just by shuffling the words and using them in different
                                                          >context with different semantics will lead nowhere.

                                                          That's why I included "GP is a subset of [...] GAs".

                                                          Regards

                                                          Martin
                                                        • C. Setzkorn
                                                          ... Would be interesting to do a comparison. Is anyone aware of one? On the other hand, one could always add some noise to the terminal parameters similar to
                                                          Message 28 of 30 , Dec 4, 2003
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                                                            > And what Candida means (I think) by the difference between parameter and
                                                            > function optimization is for example, when GP
                                                            > is used to generate the equation y = 0.75x + 0.6x^2
                                                            > GP will probably easily find y = ax + bx^2 but will probably not find
                                                            > values for 'a' and 'b' as optimal as GA would, or am I mistaken?

                                                            Would be interesting to do a comparison. Is anyone aware of one?

                                                            On the other hand, one could always add some noise to the terminal
                                                            parameters similar to GAs with real values. Perhaps one could even
                                                            represent terminals as bit strings and perform GA crossover between the
                                                            terminals that two trees, which undergo GP crossover, have in common.

                                                            I guess one could also generate suitable models using GP and then
                                                            optimise its parameters using GA.

                                                            I suppose someone must have done something like this. I would be
                                                            grateful for any references. Many thanks in advance.

                                                            Regards,
                                                            Christian
                                                          • Mats Andrén
                                                            So... This seems to be a hot topic, with little agreement indeed. I can t help but think of Langdon and Polis book Foundation of Genetic Programming where
                                                            Message 29 of 30 , Dec 4, 2003
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                                                              So...

                                                              This seems to be a hot topic, with little agreement indeed. I can't help
                                                              but think of Langdon and Polis book "Foundation of Genetic Programming"
                                                              where they state, complete with a lot of mathematic theories about the
                                                              searchspace, that GA is actually a subset of a certain kind of GP (with
                                                              one-point crossover etc). In this version of GP, the later stages of a
                                                              GP-run is actually behaving like a GA-search, according to the authors and
                                                              their mathematical proofs.

                                                              I think the theories of this book would have quite a lot to say about what
                                                              kind of efficiency we can expect GP to have on the problem mentioned here.
                                                              On the other hand, this is not the commonly used version of GP (if there is
                                                              such a version), and on a third hand (huh), I'm not good enough at
                                                              mathemathics to really assess the general validity of the mathematical
                                                              proofs in this book. Nor have I had enough time.. Maybe they are all wrong? :)

                                                              In any case, it might be a tip to some of you interested in the relation
                                                              between GA and GP, since this book explores it quite a lot.

                                                              And, correct me if I'm wrong (since I'm not too up to date about current
                                                              GA-implementations), but doesn't GA produce a fixed length bit string,
                                                              where GP produces a variable length program, that may in turn produce a
                                                              fixed length bit string (if that's what you want it to do)? Even though
                                                              GA/GP can both be used for min/max-problem-solving, they aren't really "the
                                                              same" (depending on how you define "same" of course) since you would have
                                                              to execute a program to get the results out of a GP-individual?

                                                              Have a nice day!

                                                              /Mats Andrén

                                                              At 09:11 2003-12-04 +0000, you wrote:
                                                              > > And what Candida means (I think) by the difference between parameter and
                                                              > > function optimization is for example, when GP
                                                              > > is used to generate the equation y = 0.75x + 0.6x^2
                                                              > > GP will probably easily find y = ax + bx^2 but will probably not find
                                                              > > values for 'a' and 'b' as optimal as GA would, or am I mistaken?
                                                              >
                                                              >Would be interesting to do a comparison. Is anyone aware of one?
                                                              >
                                                              >On the other hand, one could always add some noise to the terminal
                                                              >parameters similar to GAs with real values. Perhaps one could even
                                                              >represent terminals as bit strings and perform GA crossover between the
                                                              >terminals that two trees, which undergo GP crossover, have in common.
                                                              >
                                                              >I guess one could also generate suitable models using GP and then
                                                              >optimise its parameters using GA.
                                                              >
                                                              >I suppose someone must have done something like this. I would be
                                                              >grateful for any references. Many thanks in advance.
                                                              >
                                                              >Regards,
                                                              >Christian
                                                              >
                                                              >
                                                              >
                                                              >To unsubscribe from this group, send an email to:
                                                              >genetic_programming-unsubscribe@yahoogroups.com
                                                              >
                                                              >
                                                              >
                                                              >Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
                                                            • Candida Ferreira
                                                              ... I wonder what you mean by this (the commercial part, that is)... Candida Ferreira ... Candida Ferreira, Ph.D. Chief Scientist, Gepsoft 73 Elmtree Drive
                                                              Message 30 of 30 , Dec 4, 2003
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                                                                Martin Sewell wrote:

                                                                > > > Does anyone know a problem which GA can solve but GP cannot? (in a
                                                                > > > reasonable way..)
                                                                > >
                                                                > >GAs are good at parameter optimization, a task GP cannot handle, although
                                                                > >GEP, more related to GP than GAs, can handle this kind of problem very
                                                                > >well thanks to its multigenic nature (see my book <SNIP>).
                                                                >
                                                                > GP is a subset of, and more expressive than, GAs, though GAs are likely to
                                                                > be more efficient for some classes of problems. GEP, in contrast, is
                                                                > overtly more commercial.

                                                                I wonder what you mean by this (the commercial part, that is)...

                                                                Candida Ferreira

                                                                -----------------------------------------------------------
                                                                Candida Ferreira, Ph.D.
                                                                Chief Scientist, Gepsoft
                                                                73 Elmtree Drive
                                                                Bristol BS13 8NA, UK
                                                                ph: +44 (0) 117 330 9272
                                                                http://www.gepsoft.com/gepsoft
                                                                http://www.gene-expression-programming.com/author.asp
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