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Dynamic parameter control in simple evolutionary algorithms. (English) Zbl 0987.68098
Martin, Worthy N. (ed.) et al., Foundations of genetic algorithms - 6. 6th biennial meeting, FOGA-6, Charlottesville, VA, USA, 2000. Orlando, FL: Morgan Kaufmann Publishers/ Academic Press/ Harcourt. 275-294 (2002).
Summary: Evolutionary algorithms are general, randomized search heuristics that are influenced by many parameters. Though evolutionary algorithms are assumed to be robust, it is well-known that choosing the parameters appropriately is crucial for success and efficiency of the search. It has been shown in many experiments, that non-static parameter settings can be by far superior to static ones but theoretical verifications are hard to find. We investigate a very simple evolutionary algorithm and rigorously prove that employing dynamic parameter control can greatly speed-up optimization.
For the entire collection see [Zbl 0976.00032].

MSC:
68W05 Nonnumerical algorithms
68T05 Learning and adaptive systems in artificial intelligence
68P10 Searching and sorting
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