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Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems

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Book cover Genetic Programming Theory and Practice VIII

Part of the book series: Genetic and Evolutionary Computation ((GEVO,volume 8))

Abstract

Most genetic programming systems use hard-coded genetic operators that are applied according to user-specified parameters. Because it is unlikely that the provided operators or the default parameters will be ideal for all problems or all program representations, practitioners often devote considerable energy to experimentation with alternatives. Attempts to bring choices about operators and parameters under evolutionary control, through self-adaptative algorithms or meta-genetic programming, have been explored in the literature and have produced interesting results. However, no systems based on such principles have yet been demonstrated to have greater practical problem-solving power than the more-standard alternatives. This chapter explores the prospects for extending the practical power of genetic programming through the refinement of an approach called autoconstructive evolution, in which the algorithms used for the reproduction and variation of evolving programs are encoded in the programs themselves, and are thereby subject to variation and evolution in tandem with their problem-solving components. We present the motivation for the autoconstructive evolution approach, show how it can be instantiated using the Push programming language, summarize previous results with the Pushpop system, outline the more recent AutoPush system, and chart a course for future work focused on the production of practical systems that can solve hard problems.

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Authors and Affiliations

  1. Cognitive Science, Hampshire College, 01002-3359, Amherst, MA, USA

    Lee Spector

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  1. Lee Spector
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Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Saskatchewan, Canada

    Trent McConaghy

  3. , Dept. of Mathematics & Computer, University of Antwerp, Building G, Science Campus Middelhe 2020, Antwerpen, Belgium

    Ekaterina Vladislavleva

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Spector, L. (2011). Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems. In: Riolo, R., McConaghy, T., Vladislavleva, E. (eds) Genetic Programming Theory and Practice VIII. Genetic and Evolutionary Computation, vol 8. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7747-2_2

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  • DOI: https://doi.org/10.1007/978-1-4419-7747-2_2

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