On the effectiveness of crossover in simulated evolutionary optimization.
There has been renewed interest in using simulated evolution to address difficult optimization problems. These simulations can be divided into two groups: (1) those that model chromosomes and emphasize genetic operators; and (2) those that model individuals or populations and emphasize the adaptation and diversity of behavior. Recent claims have suggested that genetic models using recombination operators, specifically crossover, are typically more efficient and effective at function optimization than behavioral models that rely solely on mutation. These claims are assessed empirically on a broad range of response surfaces.
Fogel
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Natural Selection, Inc., La Jolla, CA 92037.
Stayton
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LC
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D003434:Crossing Over, Genetic; D005828:Genetics, Population; D008957:Models, Genetic; D009154:Mutation; D017711:Nonlinear Dynamics; D010641:Phenotype; D013269:Stochastic Processes