Abstract
Nonstationary function optimisation has proved a difficult area for Genetic Algorithms. Standard haploid populations find it difficult to track a moving target and tend to converge to a local optimum that appears early in a run. While it is generally accepted that various approaches involving diploidy can cope better with these kinds of problems, none of these have gained wide acceptance in the GA community. We survey a number of diploid GAs and outline some possible reasons why they have failed to gain wide acceptance, before describing a new haploid system which uses Polygenic Inheritance. Polygenic inheritance differs from most implementations of GAs in that several genes contribute to each phenotypic trait. A nonstationary function optmisation problem from the literature is described, and it is shown how various represenation scheme affect the performance of GAs on this problem.
This work is support in part by the University of Limerick Foundation
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© 1998 Springer-Verlag Berlin Heidelberg
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Ryan, C., Collins, J.J. (1998). Polygenic inheritance — A haploid scheme that can outperform diploidy. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056861
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DOI: https://doi.org/10.1007/BFb0056861
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