Competitive Self-adaptation in Evolutionary Algorithms

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Competitive Self-adaptation in Evolutionary Algorithms
Heuristic search for the global minimum is studied. This paper is focused on the adaptation of control parameters in differential evolution (DE) and in controlled random search (CRS). The competition of different control parameter settings is used in order to ensure the self-adaptation of parameter values within the search process. In the generalized CRS the self-adaptation is ensured by several competing local-search heuristics for the generation of a new trial point. DE was experimentally compared with other adaptive algorithms on a benchmark, self-adaptive CRS was compared in estimation of regression parameters on NIST nonlinear regression datasets. The competitive algorithms outperformed other algorithms both in the reliability and in the convergence rate.
Josef Tvrdík, Ivan Krivý
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Authors Josef Tvrdík, Ivan Krivý
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