Every niching method has its niche : Fitness sharing and implicit sharing compared


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Various extensions to the Genetic Algorithm (GA) attempt to find all or most optima in a search space containing several optima.Many of these emulate natural speciation. For co-evolutionary learning to succeed in a range of management and control problems, such as learning game strategies, such methods must find all or most optima. However, suitable comparison studies are rare. We compare two similar GA speciation methods, fitness sharing and implicit sharing. Using a realistic letter classification problem, we find they have advantages under different circumstances. Implicit sharing covers optima more comprehensively, when the population is large enough for a species to form at each optimum.With a population not large enough to do this, fitness sharing can find the optima with larger basins of attraction, and ignore the peaks with narrow bases, while implicit sharing is more easily distracted. This indicates that for a speciated GA trying to find as many near-global optima as possible, implicit sharing works well only if the population is large enough. This requires prior knowledge of how many peaks exist. © 1996, Springer-Verlag. All rights reserved.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN IV : International Conference on Evolutionary Computation : The 4th International Conference on Parallel Problem Solving from Nature Berlin, Germany, September 22 - 26, 1996. Proceedings
EditorsHans-Michael VOIGT, Werner EBELING, Ingo RECHENBERG, Hans-Paul SCHWEFEL
Number of pages10
ISBN (Electronic)9783540706687
ISBN (Print)9783540617235
Publication statusPublished - 1996
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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