Abstract
Cellular Genetic Algorithms (cGA) are spatially distributed Genetic Algorithms that, because of their high level of diversity, are superior to regular GAs on several optimization functions. Also, since these distributed algorithms only require communication between few closely arranged individuals, they are very suitable for a parallel implementation. We propose a new kind of cGA, called hierarchical cGA (H-cGA), where the population structure is augmented with a hierarchy according to the current fitness of the individuals. Better individuals are moved towards the center of the grid, so that high quality solutions are exploited quickly, while at the same time new solutions are provided by individuals at the outside that keep exploring the search space. This algorithmic variant is expected to increase the convergence speed of the cGA algorithm and maintain the diversity given by the distributed layout. We examine the effect of the introduced hierarchy by observing the variable takeover rates at different hierarchy levels and we compare the H-cGA to the cGA algorithm on a set of benchmark problems and show that the new approach performs promising.
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References
Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: Schaffer, J. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 428–433. Morgan Kaufmann, San Francisco (1989)
Alba, E., Tomassini, M.: Parallelism and Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms, 2nd edn. Book Series on Genetic Algorithms and Evolutionary Computation, vol. 1. Kluwer Academic Publishers, Dordrecht (2000)
Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley, Chichester (2005)
Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular evolutionary algorithms. IEEE Trans. on Evolutionary Computation 9(2), 126–142 (2005)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Systems, Man and Cybernetics - Part B 35(6), 1272–1282 (2005)
Goldberg, D., Deb, K.: Foundations of Genetic Algorithms. In: A comparative analysis of selection scheme used in genetic algorithms, pp. 69–93. Morgan Kaufmann Publishers, San Francisco (1991)
Giacobini, M., Tomassini, M., Tettamanzi, A., Alba, E.: Synchronous and asynchronous cellular evolutionary algorithms for regular lattices. IEEE Transactions on Evolutionary Computation 9(5), 489–505 (2005)
Schaffer, J., Eshelman, L.: On crossover as an evolutionary viable strategy. In: 4th ICGA, pp. 61–68. Morgan Kaufmann, San Francisco (1991)
Goldberg, D., Deb, K., Horn, J.: Massively multimodality, deception and genetic algorithms. In: Proc. of the PPSN-2, pp. 37–46. North-Holland, Amsterdam (1992)
Jong, K.D., Potter, M., Spears, W.: Using problem generators to explore the effects of epistasis. In: 7th ICGA, pp. 338–345. Morgan Kaufmann, San Francisco (1997)
Stinson, D.: An Introduction to the Design and Analysis of Algorithms. The Charles Babbage Research Center, Winnipeg, Manitoba, Canada (1985) (2nd edn., 1987)
S.K., Bäck, T., Heitkötter, J.: An evolutionary approach to combinatorial optimization problems. In: Proceedings of the 22nd annual ACM computer science conference (CSC 1994), pp. 66–73. ACM Press, New York (1994)
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Janson, S., Alba, E., Dorronsoro, B., Middendorf, M. (2006). Hierarchical Cellular Genetic Algorithm. In: Gottlieb, J., Raidl, G.R. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2006. Lecture Notes in Computer Science, vol 3906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11730095_10
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DOI: https://doi.org/10.1007/11730095_10
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