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

In this paper we present a concurrent implementation of coevolutionary genetic algorithm (GA) designed for shared memory architectures such as multi-core processor platforms. Our algorithm divides the chromosome among the processes, and not the population as it is the case for most parallel implementations 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 algorithm, such as: a synchronous or asynchronous information exchange between processes and the frequency of communication between processes. We also examine how the problem separability influences the general algorithm performance. Finally, we compare different operating systems and platforms in the evaluation process. Our paper shows that this approach is a good way to take advantage of multi-core processers and improve not only the execution time, but also the fitness in many cases.