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Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings

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Abstract

Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through it’s ability to explicitly decrease the size of the function set during evolution.

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Notes

  1. Assumes a symbiotic as opposed to a speciation model of cooperative coevolution. The latter is a distinct form of the symbiotic model, and widely used within evolutionary multi-objective optimization to encourage multiple solutions from a single population. It is thus not relevant to this work.

  2. That is, provided the noise threshold is set to anything less than 1.0, so that no column in the probability table will ever completely exclude any combination by setting the probability of its roulette selection it to 0.

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Acknowledgments

The authors gratefully acknowledge the support of a NSERC PGS-B and Izaak Walton Killam scholarship (Garnett Wilson), and the CFI New Opportunities and NSERC research grants (Dr. M. Heywood).

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  1. Faculty of Computer Science, Dalhousie University, B3H 1W5, Halifax, NS, Canada

    Garnett Wilson & Malcolm Heywood

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  1. Garnett Wilson
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Correspondence to Garnett Wilson.

Additional information

This paper is a revised and substantially extended version of: G. Wilson and M. Heywood. “Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP): A New Developmental Approach” in Proceedings of the 9th Parallel Problem Solving from Nature (PPSN IX), Reykjavik, Iceland, Runarsson et al. (eds.), Springer: Berlin, 2006, pp. 751–760.

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Wilson, G., Heywood, M. Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings. Genet Program Evolvable Mach 8, 187–220 (2007). https://doi.org/10.1007/s10710-007-9027-9

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