Serial and Parallel Genetic Algorithms as Function Optimizers

Abstract

Parallel genetic algorithms are often very different from the "traditional" genetic algorithm proposed by Holland, especially with regards to population structure and selection mechanisms. In this paper we compare several parallel genetic algorithms across a wide range of optimization functions in an attempt to determine whether these changes have positive or negative impact on their problemsolving capabilities. The findings indicate that the parallel structures perform as well as or better than standard versions, even without taking parallel hardware into account.