Neutrality and Self-Adaptation. Igel, C. & Toussaint, M. Natural Computing, 2(2):117–132, 2003.
Paper doi abstract bibtex Neutral genotype-phenotype mappings can be observed in natural evolution and are often used in evolutionary computation. In this article, important aspects of such encodings are analysed. First, it is shown that in the absence of external control neutrality allows a variation of the search distribution independent of phenotypic changes. In particular, neutrality is necessary for self-adaptation, which is used in a variety of algorithms from all main paradigms of evolutionary computation to increase efficiency. Second, the average number of fitness evaluations needed to find a desirable (e.g., optimally adapted) genotype depending on the number of desirable genotypes and the cardinality of the genotype space is derived. It turns out that this number increases only marginally when neutrality is added to an encoding presuming that the fraction of desirable genotypes stays constant and that the number of these genotypes is not too small.
@Article{Igel:2003:NC,
author = "Christian Igel and Marc Toussaint",
title = "Neutrality and Self-Adaptation",
journal = "Natural Computing",
year = "2003",
volume = "2",
number = "2",
URL = "http://ipsapp009.kluweronline.com/content/getfile/5030/5/1/abstract.htm",
doi = "doi:10.1023/A:1024906105255",
pages = "117--132",
keywords = "evolutionary computation, genotype-phenotype mapping,
neutrality, No-Free-Lunch theorem, redundancy,
self-adaptation, genetic programming",
abstract = "Neutral genotype-phenotype mappings can be observed in
natural evolution and are often used in evolutionary
computation. In this article, important aspects of such
encodings are analysed. First, it is shown that in the
absence of external control neutrality allows a
variation of the search distribution independent of
phenotypic changes. In particular, neutrality is
necessary for self-adaptation, which is used in a
variety of algorithms from all main paradigms of
evolutionary computation to increase efficiency.
Second, the average number of fitness evaluations
needed to find a desirable (e.g., optimally adapted)
genotype depending on the number of desirable genotypes
and the cardinality of the genotype space is derived.
It turns out that this number increases only marginally
when neutrality is added to an encoding presuming that
the fraction of desirable genotypes stays constant and
that the number of these genotypes is not too small.",
notes = "Article ID: 5126729",
}
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