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64062 workshops, 1 MSc, 7 FUNDED PhD positions + 1 tutorial

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  • John Woodward
    Mar 7 5:49 AM
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      Apologies for cross-posting, please forward to interested parties.

      2 workshops, 1 MSc in Big Data, 7 *funded* PhD positions, 1 tutorial.

      1. Workshop on "The Automated Design of Algorithms" (see below)


      2. Workshop on "Metaheuristic Design Patterns"

      3. New BIG DATA MSc at Stirling www.cs.stir.ac.uk/entrants/bd

      4. 7 *FUNDED* PhD positions

      5. Tutorial on "The Automated Design of Algorithms"

      4th Workshop on the Automated Design of Algorithms

      How can we automatically generate algorithms on demand? While this was
      one of the original aims of Machine Learning and Artificial
      Intelligence in the early 1950s, Genetic Programming in the early
      1990s, and more recently Genetic Improvement, existing techniques have
      fallen short of this elusive goal. This workshop will outline a number
      of steps in the right direction on the path to achieving this.

      This approach is in contrast to standard Genetic Programming which
      attempts to build programs from scratch from a typically small set of
      functions/instructions. Instead we take an already existing program(s)
      and allow evolution to improve the program. When the automatic design
      of algorithms is done using Genetic Programming (effectively "in
      vitro"), the methodology is typically referred to as a Generative
      Hyper-heuristic. When the automatic design of algorithms is done
      directly on source code (effectively "in situ"), the methodology is
      typically referred to as Genetic Improvement [5, 7].

      Although most Evolutionary Computation techniques generate specific
      solutions to a given instance of a problem, some of these techniques
      can be explored to solve more generic problems. For instance, while
      there are examples of Evolutionary Algorithms for evolving
      classification models in Data Mining or Machine Learning [1]. In other
      words, the Genetic Programming system is operating at a higher level
      of abstraction compared to how most search methodologies are currently
      employed, i.e. we are searching for solution methods, as opposed to
      solutions themselves.

      The automatic design of algorithms has some distinctions from standard
      Genetic Programming. In essence, automatic design consists of a stage
      where an algorithmic framework or template is defined and another
      stage where Genetic Programming supplies candidate algorithms to plug
      into the overall template. This approach can be identified with the
      Template Method pattern from Designed Patterns associated with Object
      Oriented programming. In short, the human provides the overall
      architecture of the algorithm (for example WHILE-loops, FOR-loops and
      IF-THEN-ELSE statements) and genetic programming fills in the details
      (for example, the bodies of the loops, or the condition and actions of
      the IF-THEN-ELSE statements). Interestingly the resulting algorithm is
      part man-made and part machine-made, and this distinguishes this
      method from standard Genetic Programming.

      Although the work in [1] consisted of evolving a complete data
      mining/machine learning algorithm, in the area of optimization this
      type of approach is named a hyper-heuristic. Hyper-heuristics are
      search methods that automatically select and combine simpler
      heuristics, creating a generic heuristic that is used to solve any
      instance of a given target type of optimization problem. Hence,
      hyper-heuristics search the space of heuristics, instead of directly
      searching in the problem solution space [2], raising the level of
      generality of the solutions produced by the hyper-heuristic
      evolutionary algorithm. For instance, a hyper-heuristic can generate a
      generic heuristic for solving any instance of the traveling salesman
      problem, involving any number of cities and any set of distances
      associated with those cities [3]; whilst a conventional evolutionary
      algorithm would just evolve a solution to one particular instance of
      the traveling salesman problem, involving a predefined set of cities
      and associated distances between them.

      Whether we name the approach for automatically designing algorithms or
      hyper-heuristics, in both cases, a set of human-designed procedural
      components or heuristics surveyed from the literature are chosen as a
      starting point (or as "building blocks" or primitive components) for
      the evolutionary search. Besides, new procedural components and
      heuristics can be automatically generated, depending on which
      components are first provided to the method.

      The main objective of this workshop is to discuss methods for
      automatically generating algorithms and/or hyper-heuristics. These
      methods have the advantage of producing solutions that are applicable
      to any instance of a problem domain, instead of a solution
      specifically produced to a single instance of the problem. The areas
      of application of these methods may include, for instance, data
      mining, machine learning, and optimization [4].

      Workshop website:



      [1] G. L. Pappa and A. A. Freitas, Automating the Design of
      Data Mining Algorithms: An Evolutionary Computation Approach,
      Springer, Natural Computing Series, 2010. xiii + 187 pages.

      [2] E. K. Burke, M. Hyde, G. Kendall and J. Woodward, A genetic
      programming hyper-heuristic approach for evolving two
      dimensional strip packing heuristics. In: IEEE Transactions on
      Evolutionary Computation, 2010.

      [3] M. Oltean and D. Dumitrescu. Evolving TSP heuristics using
      multi expression programming. In: Computational Science - ICCS
      2004, Lecture Notes in Computer Science 3037, pp. 670-673.
      Springer, 2004.

      [4] John R. Woodward and Jerry Swan, "The automatic generation of
      mutation operators
      for genetic algorithms", in Proceedings of the 14th international conference on
      Genetic and evolutionary computation conference, 2012.

      [5] William B. Langdon and Mark Harman. Genetically Improving 50000
      Lines of C++. Research Note , RN/12/09, Department of Computer
      Science, University College London, Gower Street, London WC1E 6BT, UK,

      [6] Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan.
      Automatic Design of Scheduling Policies for Dynamic Multi-objective
      Job Shop Scheduling via Cooperative Coevolution Genetic Programming.
      IEEE Transactions on Evolutionary Computation. Accepted for future

      [7] Justyna Petke, Mark Harman, William B. Langdon, and Westley Weimer
      Using Genetic Improvement & Code Transplants to Specialise a C++
      Program to a Problem Class Proceedings of the 17th European Conference
      on Genetic Programming, EuroGP 2014, Granada, Spain, 2014. Springer

      Important Dates

      Paper submission deadline: March 28th

      Notification of acceptance: April 15th

      Camera-ready deadline: April 25th

      Registration deadline: TBA

      Paper Submission

      Submitted papers should follow the ACM format, and not exceed 8 pages.
      Please see the GECCO 2014 information for authors for further details.
      However, note that the review process of the workshop is not
      double-blind. Hence, authors' information should appear in the paper.

      All accepted papers will be presented at the workshop and appear in
      the GECCO workshop volume. Proceedings of the workshop will be
      published on CD-ROM, and distributed at the conference.

      Papers should be submitted in PostScript or PDF format to: [jrw at cs
      dot stir dot ac dot uk], and contain the subject "GECCO 2014
      Workshop". A conformation of recipt email will be sent by return


      This will be a half-day workshop. Each presentation is planned to last
      for 20 minutes followed by 10 minutes for discussions, and the panel
      will last 45 minutes.

      Workshop Chairs

      John Woodward - University of Stirling, United Kingdom

      Jerry Swan - University of Stirling, United Kingdom

      Earl Barr - University College London, United Kingdom


      John Woodward - jrw at cs dot stir dot ac dot uk

      Jerry Swan - jsw at cs dot stir dot ac dot uk