Massively Parallel Generational Ga On Gpgpu Applied To Power Load Profiles Determination

Evolutionary algorithms are capable of solving a wide range of different optimization problems including real world ones. The latter, however, often require a considerable amount of computational power. Parallelization over powerful GPGPU cards is a way to tackle this problem, but this remains hard to do due to their specificities. Parallelizing the fitness function only yields good results if it dwarfs the rest of the evolutionary algorithm. Otherwise, parallelization overhead and Amdahl's law may ruin this effort.In this paper, we will show how completely parallelizing an evolutionary algorithm can help solving a large real world electrical problem with a lightweight evaluation function without quality loss.