Explicit parallelism of genetic algorithms through population structures

Abstract

Genetic algorithms imitate the collective learning paradigm found in living nature. They derive their power largely from their implicit parallelism gained by processing a population of points in the search space simultaneously. In this paper, we describe an extension of genetic algorithms making them also explicitly parallel. The advantages of the introduction of a population structure are twofold: firstly, we specify an algorithm which uses only local rules and local data making it massively parallel with an observed linear speedup on a transputer-based parallel system, and secondly, our simulations show that both convergence speed and final quality are improved in comparison to a genetic algorithm without population structure.