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

Author

Spector, Lee

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. [ABSTRACT FROM AUTHOR]

Published
2011
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31. Multidimensional Tags, Cooperative Populations, and Genetic Programming.

Author

Goldberg, David E., Koza, John R., Riolo, Rick, Soule, Terence, Worzel, Bill, Spector, Lee, and Klein, Jon

Abstract

We present new results on the evolution of tag-mediated cooperation, demonstrating that the use of multidimensional tags can enhance the emergence of high levels of cooperation. We discuss these results in the context of prior cases in which work on the evolution of cooperation has led to practical techniques for improving the problem-solving performance of genetic programming systems. [ABSTRACT FROM AUTHOR]

Published
2007
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