Genotype Division for Shared Memory Parallel Genetic Algorithm Across Platforms and Systems

Abstract

In this paper we present a concurrent implementation of coevolutionary genetic algorith m (GA ) designed for shared memo ry architectures such as multi-core processor platforms. Our algorith m div ides the chromosome among the processes, and not the population as it is the case for most parallel imp lementations of the GA. This approach results in a division of the problem to be solved by the GA into sub-problems. We analyze the influence on performance and speedup of several parameters defining the algorith m, such as: a synchronous or asynchronous informat ion exchange between processes and the frequency of communication between processes. We also examine how the problem separability influ- ences the general algorith m performance. Finally, we co mpare different operating systems and platforms in the evaluation process. Our paper shows that this approach is a good way to take advantage of mu lti-core processers and improve not only the execution time, but also the fitness in many cases.