Comparative analysis of intra-algorithm parallel multiobjective evolutionary algorithms: Taxonomy implications on bioinformatics scenarios

Abstract

Parallelism has become a recurrent tool to support computational intelligence and, particularly, evolutionary algorithms in the solution of very complex optimization problems, especially in the multiobjective case. However, the selection of parallel evolutionary designs often represents a difficult question due to the multiple variables that must be considered to attain an accurate exploitation of hardware resources, along with their influence in solution quality. This work looks into this issue by conducting a comparative performance analysis of intra-algorithm parallel multiobjective evolutionary algorithms running on shared-memory configurations. We consider different design trends including A) generational approaches based on measurements of solution quality plus diversity, B) generational approaches based on measurements of solution quality exclusively, and C) non-generational approaches. Following these trends, a total of six representative algorithms are applied to tackle a challenging bioinformatics problem as a case study, phylogenetic reconstruction. Experimentation on real-world scenarios point out the main advantages and weaknesses of each design, outlining guidelines for the selection of methods according to the characteristics of the employed hardware, evolutionary properties, and the parallelism exploitation capabilities of the evaluated approaches.