Abstract
Coevolutionary algorithm takes advantage of the reduced search space by evolving species associated with subsets of variables independently but cooperatively. In this paper we propose an efficient coevolutionary algorithm combining species splitting and merging together. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with several benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms existing coevolutionary algorithms.
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References
Potter, M.A., DeJong, K.A.: A cooperative coevolutionary approach to function optimization. In: Proc. of the Third Conference on Parallel Problem Solving from Nature, pp. 249–257. Springer, Heidelberg (1994)
Potter, M.A., DeJong, K.A.: Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Mundhe, M., Sen, S.: Evolving agent societies that avoid social dilemmas. In: Proc. Of GECCO 2000, Las Vegas, Nevada, July 2000, pp. 809–816 (2000)
Pagie, L., Mitchell, M.: A Comparison of Evolutionry and Coevolutionary Search. Journal of Computational Intelligence and Applications 2(1), 53–69 (2002)
Weicker, K., Weicker, N.: On the improvement of coevolutionary optimizers by learning variable interdependencies. In: Congress on Evolutionary Computation (CEC 1999), pp. 1627–1632 (1999)
Nash, J.: Non-cooperative games. Annals of Mathematics 5(2), 286–295 (1951)
Salomon, R.: Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39, 210–229 (1996)
Eriksson, R., Olsson, B.: Cooperative Coevolution in Inventory Control Optimisation. In: Proc. of 3rd International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 1997), Norwich, UK (April 1997)
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Kim, M.W., Ryu, J.W. (2004). Species Merging and Splitting for Efficient Search in Coevolutionary Algorithm. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_36
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DOI: https://doi.org/10.1007/978-3-540-28633-2_36
Publisher Name: Springer, Berlin, Heidelberg
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