Although most evolutionary computation techniques are designed to 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 many examples of evolutionary algorithms for evolving classification models in data mining or machine learning, the work described in [1] used a genetic programming algorithm to create a generic classification algorithm which will, in turn, generate a specific classification model for any given classification dataset, in any given application domain.

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 in the space of heuristics, instead of searching in the problem solution space [2,3], raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. For instance, a hyper-heuristics can generate a generic heuristics for solving any instance of the traveling salesman problem, involving any number of cities and any set of distances associated with those cities [4]; 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 it an 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") 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 evolutionary computation methods for automatic generation of algorithms or heuristics**. Instead of using evolutionary computation to evolve solutions, these methods evolve methodologies that can be applied to future problems, after the evolution process has finished. We aim to discuss all aspects of the automatic design of algorithms. The areas of application of these algorithms may include, for instance, data mining, machine learning, optimization, bioinformatics, image processing, economics, etc.

[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] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan and J. R. Woodward, Exploring Hyper-heuristic Methodologies with Genetic Programming, Computational Intelligence: Collaboration, Fusion and Emergence, In C. Mumford and L. Jain (eds.), Intelligent Systems Reference Library, Springer, pp. 177-201, 2009

[4] 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.

**Paper submission deadline: March 28th**

- Notification of acceptance: April 9th

- Camera-ready deadline: April 16th

- Registration deadline: May 4th

Submitted papers should follow the ACM format, and not exceed 8 pages. Please see the GECCO 2012 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: [john.woodward at nottingham dot edu dot cn], and contain the subject "GECCO Workshop".

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.

8.30 Workshop Introduction

9.20 Evolving Evolutionary Algorithms, Nuno Lourenco, Francisco B. Pereira and Ernesto Costa

9.50 Supportive Coevolution, Brian W. Goldman and Daniel R. Tauritz

10.20 Coffee Break

10.40 The Automatic Generation of Mutation Operators for Genetic Algorithms, John Woodward and Jerry Swan

11.10 Autoconstructive Evolution for Structural Problems, Kyle Harrington, Lee Spector, Jordan Pollack and Una-May O’Reilly

11.40 Discussion Panel

12.30 Wrap up and Conclusions

John Woodward - University of Nottingham, Ningbo, China

Gisele L. Pappa - UFMG(Federal University of Minas Gerais), Brazil

Matthew R. Hyde - University of Nottingham, United Kingdom

Jerry Swan - University of Nottingham, United Kingdom

- John Woodward - john.woodward at nottingham dot edu dot cn

Although most evolutionary computation techniques are designed to generate specific solutions to a given instance of a problem, some of these techniques can be explored to solve more generic problems. The main objective of this workshop is to discuss evolutionary computation methods for generating generic algorithms and/or 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 for a single instance of the problem. The areas of application of these methods may include, for instance, data mining, machine learning, optimization, bioinformatics, image processing, economics, etc.

The workshop welcomes original submissions on all aspects of Evolutionary Computation for Designing Generic Algorithms, which include (but are not limited to) the following topics and themes:

- Evolutionary algorithms for designing generic combinatorial optimization algorithms or heuristics
- Evolutionary algorithms for designing generic machine learning algorithms or heuristics
- Evolutionary algorithms for designing generic function optimization
- Evolutionary algorithms for designing generic algorithms or heuristics for bioinformatics
- (Meta-level) evolutionary algorithms for designing other (base-level) evolutionary algorithms
- Empirical comparison of different hyper-heuristics
- Theoretical analyses of hyper-heuristics
- Automatic selection of algorithms' building blocks as a preprocessing step for the use of hyper-heuristics
- Analysis of the trade-off between generality and effectiveness of different heuristics algorithms or heuristics produced by hyper-heuristics
- Real-world applications of hyper-heuristics

Last updated on 29 May, 2012