An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments

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. Categories and Subject Descriptors I.2.8 [Computing Methodologies]: Artificial Intelligence— Problem Solving, Control Methods, and Search General Terms Algorithms, Experimentation Keywords Evolution ...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Oliver Kramer, Bartek Gloger, Andreas Goebels
• Keywords: GECCO 2007, Optimal Parameters, Parameter Settings, Parameters