Conference Paper

Statistical Analysis of Parameter Setting in Real-Coded Evolutionary Algorithms

DOI: 10.1007/978-3-642-15871-1_46 Conference: Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, Kraków, Poland, September 11-15, 2010. Proceedings, Part II
Source: DBLP

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.

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    • "Finally, Rojas et al. [55] study the effects of parameters involved in GA design by applying ANOVA, while Castillo et al. [56] use statistical tools to find accurate parameter values involved in the design of a neuro-genetic algorithm. However, methodology presented in these papers [57], could be improved by means of post-hoc statistical tests. We propose a complete methodology based on parametric statistical tools to analyze the parameters with a higher influence on the performance for a given optimization method. "
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