Abstract
In this article we introduce a general framework for constructing an adaptive genotype-to-phenotype mapping, and apply it to developmental genetic programming. In this preliminary investigation, we run a series of comparative experiments on a simple test problem. Our results show that the adaptive algorithm is able to outperform its non-adaptive counterpart.
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Wolfgang Banzhaf. Genotype-phenotype-mapping and neutral variation-a case study in genetic programming. In Y. Davidor, H.-P. Schwefel, and R. Männer, editors, Proceedings of Parallel Problem Solving from Nature III, pages 322–332, Berlin, 1994. Springer.
David E. Goldberg. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., 1989.
Robert E Keller and Wolfgang Banzhaf. Genetic programming using genotypephenotype mappings from linear genomes to linear phenotypes. In John R Koza, David E Goldberg, David B Fogel, and Rick L Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, Cambridge, USA, 1996. MIT Press.
Robert E Keller and Wolfgang Banzhaf. The evolution of genetic code in genetic programming. In Wolfgang Banzhaf, Jason Daida, Agoston E Eiben, Max H Garzon, Mark Jakiela, and Robert E Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1077–1082, San Francisco, California, 1999. Morgan Kaufmann. ISBN 1-55860-611-4.
John R. Koza. Genetic Programming-On the Programming of Computers by Means of Natural Selection. The MIT Press, Massachusetts, Cambridge, 1992. ISBN 0-262-11170-5.
William B. Langdon and Ricardo Poli. An analysis of the MAX problem in genetic programming. In John R. Koza, K. Deb, Marco Darigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, GP97: Proceedings of the Second Annual Conference on Genetic Programming, pages 222–230, Stanford University, USA, July 1997. Morgan-Kaufmann.
Steve Margetts and Antonia J. Jones. Phlegmatic mappings for function optimisation with genetic algorithms. In Darrell Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 82–89, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.
Timothy Perkis. Stack-based genetic programming. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence, pages 148–153. IEEE Press, 1994.
Mitchell A. Potter. The Design and Analysis of a ComputationalModel of Cooperative Coevolution. Phd thesis, George Mason University, Fairfax, Virginia, Spring 1997. Supervised by Kenneth A.De Jong.
Robert Sedgewick. Algorithms in C++. Addison-Wesley Publishing Company Inc., 1992. ISBN 0-201-51059-6.
Lee Spector and Kilian Stoffel. Ontogenetic programming. In John R Koza, David E Goldberg, David B Fogel, and Rick L Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, Cambridge MA, 1996.MIT Press.
Kilian Stoffel and Lee Spector. High-performance, parallel, stack-based genetic programming. In John R Koza, David E Goldberg, David B Fogel, and Rick L Riolo editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 224–229, Cambridge MA, 1996. The MIT Press.
Astro Teller. Advances in Genetic Programming II, chapter 3: Evolving Programmers: The Co-evolution of Intelligent Recombination Operators. MIT Press, 1996.
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Margetts, S., Jones, A.J. (2001). An Adaptive Mapping for Developmental Genetic Programming. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_9
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DOI: https://doi.org/10.1007/3-540-45355-5_9
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