Parallel Optimization of Evolutionary Algorithms
نویسنده
چکیده
A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization). The genetic algorithms which result from the meta-evolution experiment are considerably faster than standard genetic algorithms and connrm recent theoretical results about optimal mutation rates and the interaction of selective pressure and mutation rate.
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