Determining Relative Importance and Best Settings for Genetic Algorithm Control Parameters

This paper makes two main contributions: (1) we define an experiment design and analysis approach that can be adapted to determine relative importance and best settings for control parameters in any evolutionary computation algorithm and (2) we determine the relative importance and best settings for the control parameters of a classic genetic algorithm (GA). We plan to use these control settings to parameterize a GA applied to steer a population of cloud-computing simulators into behavioral directions that reveal degraded performance and system collapse. Such a method could serve as a design tool, empowering system engineers to identify and mitigate low-probability, costly failure scenarios. In the existing GA literature, we uncovered conflicting opinions and evidence regarding key GA control parameters, and the best settings to adopt. Consequently, we designed and executed an experiment to determine the relative importance and best settings for seven GA control parameters, when applied across a set of numeric optimization problems drawn from the literature. This paper describes our experiment design, analysis methods and results. We found that crossover most significantly influences GA success, followed by mutation rate and reboot point, with population size ranking third. Elite selection and selection method ranked fourth, while precision used within the chromosome to represent numerical values had least influence. Our findings are robust over 60 numeric optimization problems.