Titlebook: Parallel Problem Solving from Nature - PPSN VII; 7th International Co Juan Julián Merelo Guervós,Panagiotis Adamidis,Jos Conference proceed

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Parallel Varying Mutation in Deterministic and Self-adaptive GAsself-adaptive varying mutation GAs. After comparative experiments, we found that varying mutation parallel to crossover can be a more effective framework in both deterministic and self-adaptive GAs to achieve faster convergence velocity and higher convergence reliability. Best performance is achieve

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On Fitness Distributions and Expected Fitness Gain of Mutation Rates in Parallel Evolutionary Algorithms large-population-parallelism. We justify the notion that high rates achieve better results, using underlying theory which notices that parallelization favourably alters the fitness distribution of a mutation operator. We derive an expression which sets out how this is changed in terms of the level

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Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programmingrial (subtrees). The mechanism by which the crossover operator achieves success in GP, and even whether crossover does in fact exhibit relative success compared to other operators such as mutation, is anything but clear [.]. An intuitive explanation for successful crossover would be that the operato

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