Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Global Optimization by Means of Distributed Evolution Strategies
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Automatic and interactive tuning of algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Information Sciences: an International Journal
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The success of evolutionary algorithms (EAs) depends crucially on finding suitable parameter settings. Doing this by hand is a very time consuming job without the guarantee to finally find satisfactory parameters. Of course, there exist various kinds of parameter control techniques, but not for parameter tuning. The Design of Experiment (DoE) paradigm offers a way of retrieving optimal parameter settings. It is still a tedious task, but it is known to be a robust and well tested suite, which can be beneficial for giving reason to parameter choices besides human experience. In this paper we analyse evolution strategies (ES) and particle swarm optimisation (PSO) with and without optimal parameters gathered with DoE. Reasonable improvements have been observed for the two ES variants.