Factorial design analysis applied to the performance of parallel evolutionary algorithms

Mônica S Pais,Edmilson R Pinto,Igor S Peretta,Keiji Yamanaka

doi:10.1186/1678-4804-20-6

Abstract

BackgroundParallel computing is a powerful way to reduce computation time and to improve the quality of solutions of evolutionary algorithms (EAs). At first, parallel EAs (PEAs) ran on very expensive and not easily available parallel machines. As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding EAs to turn into parallel programs and exploit the power of multicores. The parallel implementation brings more factors to influence performance and consequently adds more complexity on PEA evaluations. Statistics can help in this task and can guarantee the significance and correct conclusions with minimum tests, provided that the correct design of experiments is applied.MethodsWe show how to guarantee the correct estimation of speedups and how to apply a factorial design on the analysis of PEA performance.ResultsThe performance and the factor effects were not the same for the two benchmark functions studied in this work. The Rastrigin function presented a higher coefficient of variation than the Rosenbrock function, and the factor and interaction effects on the speedup of the parallel genetic algorithm I (PGA-I) were different in both.ConclusionsAs a case study, we evaluate the influence of migration related to parameters on the performance of the parallel evolutionary algorithm solving two benchmark problems executed on a multicore processor. We made a particular effort in carefully applying the statistical concepts in the development of our analysis.

Highlights

Parallel computing is a powerful way to reduce computation time and to improve the quality of solutions of evolutionary algorithms (EAs)
Genetic algorithms (GA) are a widely used subfamily of EAs, which are stochastic search methods designed for exploring complex problem spaces in order to find optimal solutions using minimal information on the problem to guide the search
This paper introduced a method to guarantee the correct estimation of speedups and the application of a factorial design on the analysis of parallel EAs (PEAs) performance

Summary

Introduction

Parallel computing is a powerful way to reduce computation time and to improve the quality of solutions of evolutionary algorithms (EAs). Parallel EAs (PEAs) ran on very expensive and not available parallel machines. As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding EAs to turn into parallel programs and exploit the power of multicores. EAs require computational power, and there have been efforts to improve their performance through parallel implementation [2,3]. For parallel EAs (PEAs), it is possible to achieve superlinear speedups [4]. Evaluations of parallel algorithms adopt a widely used performance measure called speedup. As PEAs are randomized algorithms, measures are usually averages. We show how to guarantee the correct estimation of the average speedups

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