Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Investigating the success of spatial coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Takeover time curves in random and small-world structured populations
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Empirical analysis of a genetic algorithm-based stress test technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Genetic algorithm (GA) has been widely used in optimizing and solving various problems since first proposed, and its characters also have been deeply studied. In this paper, we investigate the benefits of genetic algorithm whose population is distributed on small-world networks. In particular, we pay our attention to the complexity of how small-world affects the behavior of spatial GA. Our work shows that, on a complex problem, the behavior of spatial GA on the small-world networks is influenced by at least two different factors: local selection and asymmetric topology. It is more complex than previous results from simple lattice models. Our results could provide lots of potential methods to improve the performance of spatial GA and give some guidance for designing of parallel genetic algorithm. We also present many future problems on the influence of small-world to spatial GA.