Statistical analysis of parameter setting in real-coded evolutionary algorithms

Authors:
Maria I. García Arenas;Pedro Ángel Castillo Valdivieso;Antonio M. Mora García;Juan J. Merelo Guervós;Juan L. Jiménez Laredo;Pablo García-Sánchez
Affiliations:
Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Year:
2010

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Abstract

When evolutionary algorithm (EA) applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of the algorithm. This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of an EA method and the different parameters it requires, such as those related to the number of generations, population size, operators application and selection type. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.