Preserving variability in sexual multi-agent systems with diploidy and dominance
ESAW'05 Proceedings of the 6th international conference on Engineering Societies in the Agents World
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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This paper describes a number of different coarse-grain GA's, including various migration strategies and connectivity schemes to address the premature convergence problem. These approaches are evaluated on a graph partitioning problem. Our experiments showed, first, that the sequential GA's used are not as effective as parallel GA's for this graph partition problem. Second, for coarse-grain GA's, the results indicate that using a large number of nodes and exchanging individuals asynchronously among them is very effective. Third, GA's that exchange solutions based on population similarity instead of a fixed connection topology get better results without any degradation in speed. Finally, we propose a new coarse-grained GA architecture, the Injection Island GA (iiGA). The preliminary results of iiGA's show them to be a promising new approach to coarse-grain GA's.