Genetic algorithms and grid computing for artificial embryogeny

Abstract : Genetic algorithms are very demanding in terms of computing time and, when the population size is large, they need days to complete or even fail due to memory restrictions. It is particularly the case for artificial life where each evaluation can take more than one minute to develop an artificial creature, plant or organism. Indeed, creatures are developed in physical and chemical simulators that require important computation resources. In order to create more and more realistic creatures, we propose a grid parallelized version of genetic algorithms. Two possibilities exist to increase them: supercomputers or computational grids. Because of their scalability, we choose computational grid in their works.