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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    ABSTRACT: It is very important when search methods are being designed to know which parameters have the greatest influence on the behaviour and performance of the algorithm. To this end, algorithm parameters are commonly calibrated by means of either theoretic analysis or intensive experimentation. However, due to the importance of parameters and its effect on the results, finding appropriate parameter values should be carried out using robust tools to determine the way they operate and influence the results. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameters that are statistically significant. In this paper the ANOVA ANalysis Of the VAriance method is used to carry out an exhaustive analysis of an evolutionary algorithm method and the different parameters it requires. Following this idea, the significance and relative importance of the parameters regarding the obtained results, as well as suitable values for each of these, were obtained using ANOVA and post-hoc Tukey's Honestly Significant Difference tests on four well known function optimization problems. Through this statistical study we have verified the adequacy of parameter values available in the bibliography using parametric hypothesis tests.
    Intelligent Data Analysis 09/2013; 17(5):771-789. · 0.47 Impact Factor

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