Finding Needles in Haystacks Is Not Hard with Neutrality

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Finding Needles in Haystacks Is Not Hard with Neutrality (2002)

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by Tina Yu , Julian Miller

Venue:	Proceedings of the 5th European Conference on Genetic Programming. Springer LNCS 2278
Citations:	23 - 5 self

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@INPROCEEDINGS{Yu02findingneedles,
    author = {Tina Yu and Julian Miller},
    title = {Finding Needles in Haystacks Is Not Hard with Neutrality},
    booktitle = {Proceedings of the 5th European Conference on Genetic Programming. Springer LNCS 2278},
    year = {2002},
    pages = {13--25}
}

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Abstract

Abstract. We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral networks do not give performance improvement, no impairment occurs either. We also tested a hypothesis proposed in our previous work. The results support the hypothesis: when the ratio of adaptive/neutral mutations during neutral walk is close to the ratio of adaptive/neutral mutations at the fitness improvement step, the evolutionary search has a high success rate. Moreover, the ratio magnitudes indicate that more neutral mutations (than adaptive mutations) are required for the algorithms to find a solution in this type of search space. 1

Keyphrases

neutral network adaptive neutral mutation search space fitness improvement step high success rate experimental result ratio magnitude evolutionary search needle-in-haystack fitness landscape previous work neutral walk search success rate different problem performance improvement adaptive mutation evolutionary algorithm neutral mutation

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