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Re: GA/GP and present industry market

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  • Mostafa Kalantar
    Hi, you shouldn t compare a specific branch of computational intelligence (CI) like GA/GP with technologies like WWW or VLSI which include a broad spectrum of
    Message 1 of 15 , Aug 2, 2008
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      Hi,
      you shouldn't compare a specific branch of computational intelligence (CI) like
      GA/GP with technologies like WWW or VLSI which include a broad spectrum of sub-technologies.
      In fact you can compare GA/GP with other CI/Machine Learning approaches like Neural
      Networks, Fuzzy Logic, or statistical methods like Support Vector Machines.

      Pay
      attention that Evolutionary Algorithms can be applied as an optimization tool
      in a variety of fields including WWW and VLSI, or as a machine learning method.

       

      For
      industrial case-studies see the books:

      1.     
      Soft
      Computing Applications in Industry , Springer

      2.     
      Intelligent Control Systems Using Soft
      Computing

      3.     
      Fusion of Neural Networks, Fuzzy Systems
      and Genetic Algorithms: Industrial Applications

      4.     
      Industrial Applications of Genetic
      Algorithms






      [Non-text portions of this message have been removed]
    • Peter Ross
      Hi [private reply], ... I think the issue is that genetic algorithms and evolutionary optimization are too specific; VLSI , Data Mining ,
      Message 2 of 15 , Aug 3, 2008
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        Hi [private reply],

        On Thursday 31 July 2008, Khaled Ahsan Polin wrote:

        > Yes, I did google but 90% of the entries are on academic research and
        > experimentation on some toy problems. I was hoping to see some serious
        > research in industry. Actually I am looking for some research jobs in
        > industry. But when I put "Genetic Algorithm" or "Evolutionary
        > Optimization", as key words in job sites, I see very small number of jobs
        > from some small game companies. On the other hand, this result is totally
        > different when I put "VLSI", "Data Mining", "Bioinformatics", "Grid
        > Computing" etc.

        I think the issue is that "genetic algorithms" and "evolutionary optimization"
        are too specific; "VLSI", "Data Mining", "Bioinformatics", "Grid Computing"
        are each broad categories. For example, if a company seeks someone with
        talents in data mining, they would expect good applicants to be knowledgeable
        about many computing techniques (including GAs, and maybe also GP), and also
        know a good deal about statistics, data processing and cleaning and so on.

        Companies are unlikely to advertise explicitly for expertise in GAs or
        evolutionary methods, they would usually seek someone who is also familiar
        with many other relevant techniques and who might have some sense of what's
        best to use in any given circumstance and so might advertise for talents in
        optimi[sz]ation, logistics, algorithm development and so on. Companies have to
        be careful what they claim to be wanting, because once employed the person
        has to be kept fed with work.

        Although many universities offer components of computing or engineering
        degrees that include significant stuff on evolutionary and bio-inspired
        methods, they are not necessarily good at producing what industry wants.
        It can be much easier and much cheaper to train several existing staff
        in bio-inspired methods, perhaps by getting a consultant in for a while, than
        to hire a new graduate and get them fitted into the company's working habits.
        Hiring a lone person purely for specialist knowledge not shared by those
        he/she will have to work alongside is often a recipe for discontent.

        Although a lot of source code is produced by universities, a lot of it
        is buggy and/or poorly documented and as such is not a great recommendation
        -- I should know, I produced some of that myself in 20+ years at Edinburgh
        University. It was a real eye-opener to observe that there were many grateful
        users of some of the GA software I wrote, but not one of them spotted some
        significant bugs that had been in the code for years before I noticed
        them :-(, or if they did they didn't mention them.

        Here is the most recent example I found in some public code produced by a
        university research team at a highly-rated research university. The aim
        of the fragment is to produce uniform-random permutations of 0..N-1; the
        fragment has been rewritten by me to simplify it and to protect the original
        author from shame:
        for(i=0; i<N; i++)
        a[i] = i; // a[] has been made big enough
        for(i=0; i<N; i++) {
        j = random(N); // uniform-random int in 0..N-1 inclusive
        if(i != j)
        swap(a[i], a[j]); // something that correctly
        // swaps the two values
        }
        See the problem? It's not obvious, but this produces a significantly
        biased sampling. I run a business, and I although I might mention
        GA/GP in some further particulars of a job advert, I would also care
        a lot about hiring someone who can avoid such mistakes. So I might look for
        someone with a broader outlook and with evidence of good practical skills,
        rather than trying specifically to target people looking for a job that
        involves GA/GP. I might want good people who know a bit about GA/GP, but
        that's probably not what I would say in an advert that I was wanting.

        Best wishes,

        Peter Ross
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