Finding Needles in Haystacks Is Not Hard with Neutrality Export

@inproceedings{citeulike:2945469, 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.}, address = {London, UK}, author = {Yu, Tina and Miller, Julian F.}, booktitle = {EuroGP '02: Proceedings of the 5th European Conference on Genetic Programming}, citeulike-article-id = {2945469}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=704112}, isbn = {3540433783}, keywords = {evolution, evolvability, genetic-programming, neutrality}, pages = {13--25}, posted-at = {2008-06-30 17:25:53}, priority = {2}, publisher = {Springer-Verlag}, series = {Lecture Notes In Computer Science}, title = {Finding Needles in Haystacks Is Not Hard with Neutrality}, url = {http://portal.acm.org/citation.cfm?id=704112}, volume = {2278}, year = {2002} }

TY - CONF ID - citeulike:2945469 L3 - citeulike-article-id:2945469 N2 - 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. SE - Lecture Notes In Computer Science SN - 3540433783 AD - London, UK EP - 25 TI - Finding Needles in Haystacks Is Not Hard with Neutrality PB - Springer-Verlag VL - 2278 T2 - EuroGP '02: Proceedings of the 5th European Conference on Genetic Programming SP - 13 KW - evolution KW - evolvability KW - genetic-programming KW - neutrality AU - Yu, Tina AU - Miller, Julian PY - 2002/// UR - http://portal.acm.org/citation.cfm?id=704112 ER -