Estudo da influência dos parâmetros de algoritmos paralelos da computação evolutiva no seu desempenho em plataformas multicore

Abstract

Parallel computing is a powerful way to reduce the computation time and to improve the quality of solutions of evolutionary algorithms (EAs). At first, parallel evolutionary algorithms (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 PEAs evaluations. Statistics can help in this task and guarantee the significance and correct conclusions with minimum tests, provided that the correct design of experiments is applied. This work presents a methodology that guarantees the correct estimation of speedups and applies a factorial design on the analysis of PEAs performance. As a case study, the influence of migration related parameters on the performance of a parallel evolutionary algorithm solving two benchmark problems executed on a multicore processor is evaluated.