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.
Keywords
- Variable Interdependency
- Nash Equilibrium Point
- Cooperative Coevolution
- Coevolutionary Algorithm
- Rosenbrock Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2004 Springer-Verlag Berlin Heidelberg
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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
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
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