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GECCO
2006
Springer

"Optimal" mutation rates for genetic search

13 years 7 months ago
"Optimal" mutation rates for genetic search
Using a set of model landscapes we examine how different mutation rates affect different search metrics. We show that very universal heuristics, such as 1/N and the error threshold, can generally be improved upon if one has some qualitative information about the landscape. In particular, we show in the case of multiple optima (signals) how mutation affects which signal dominates and how passing between the dominance of one to another depends on the relative height and size of the peaks and their relative positions in the configuration space. Categories and Subject Descriptors I.2.m [Computing Methodologies]: Artificial Intelligence-Miscellaneous General Terms Performance Keywords Genetic Algorithms, Selection, Mutation rate, Effective Fitness, Error threshold
Jorge Cervantes, Christopher R. Stephens
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Jorge Cervantes, Christopher R. Stephens
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