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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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An Empirical Study of Multipopulation Genetic Programming
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EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
An island model for high-dimensional genomes using phylogenetic speciation and species barcoding
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Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey and taxonomy of performance improvement of canonical genetic programming
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Information Sciences: an International Journal
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Topological effects on the performance of island model of parallel genetic algorithm
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
An analysis of the migration rates for biogeography-based optimization
Information Sciences: an International Journal
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This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a canonical parallel EA.