Parallel genetic algorithms for a hypercube
Reiko Tanese · international conference on Genetic algorithms · 1987
This paper discusses a parallel genetic algorithm for a medium-grained hypercube computer. Each processor runs the genetic algorithm on its own sub-population, periodically selecting the best individuals from the sub-population and sending copies of them to one of its neighboring processors. The performance of the parallel algorithm on a function maximization problem is compared to the performance of the serial version. The parallel algorithm achieves comparable results with near-linear speed-up. In addition, some experiments were performed to study the effects of varying the parameters for the parallel model.