Divide and Conquer in Coevolution: A Difficult Balancing Act
In recent years, Cooperative Coevolutionary Evolutionary Algorithms (CCEAs) have been developed as extensions to traditional Evolutionary Algorithms (EAs). CCEAs attempt to solve the optimization problems by decomposing them into subcomponents referred to as collaborators. CCEAs have been deemed attractive for certain complex problems (with high number of decision variables), as they can achieve better fitness values than traditional EAs by employing “divide and conquer” strategy. However, their performance can vary from good to bad depending on the choice of collaborators, separability of problem and the underlying recombination scheme. This chapter highlights that a basic CCEA is inadequate to handle a wide variety of problems. Thereafter, a CCEA with adaptive partitioning (CCEA-AVP) has been introduced, which attempts to chose the collaborators adaptively during the search, depending on the relationships between the design variables. Studies have been done on various test functions and the proposed technique has been compared with conventional EA as well as conventional CCEA to highlight the benefits. A number of areas of further research in CCEA are highlighted to fully exploit the benefits of coevolution.
KeywordsCorrelation Threshold Cooperative Coevolution Convergence Plot Large Scale Optimization Problem Collaboration Strategy
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- 2.Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13(1) (2009)Google Scholar
- 3.Liu, B., Ma, H., Zhang, X.: A coevolutionary differential evolution algorithm for constrained optimization. In: Proc. of Third International Conference on Natural Computation, pp. 51–57 (2007)Google Scholar
- 6.Popovici, E., De Jong, K.: Relationships between internal and external metrics in co-evolution. In: IEEE Congress on Evolutionary Computation CEC 2005, vol. 3, pp. 2800–2807 (2005), doi:10.1109/CEC.2005.1555046Google Scholar
- 8.Popovici, E., De Jong, K.: The effects of interaction frequency on the optimization performance of cooperative coevolution. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 353–360. ACM, New York (2006), http://doi.acm.org/10.1145/1143997.1144061 CrossRefGoogle Scholar
- 9.Popovici, E., De Jong, K.: Sequential versus parallel cooperative coevolutionary algorithms for optimization. In: IEEE Congress on Evolutionary Computation CEC 2006, pp. 1610–1617 (2006), doi:10.1109/CEC.2006.1688501Google Scholar
- 10.Potter, M.A., Jong, K.A.D.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)Google Scholar
- 11.Ray, T., Yao, X.: A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning. In: Proceedings of 2009 IEEE Congress on Evolutionary Computation CEC 2009, pp. 983–989 (2009)Google Scholar
- 12.Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Tech. rep., Nanyang Technological University, Singapore and Kanpur Genetic Algorithms Laboratory, IIT Kanpur, India (2005)Google Scholar
- 13.Tan, C., Goh, C., Tan, K., Tay, A.: A cooperative coevolutionary algorithm for multiobjective particle swarm optimization. In: IEEE Congress on Evolutionary Computation CEC 2007, pp. 3180–3186 (2007), doi:10.1109/CEC.2007.4424878Google Scholar
- 14.Van den Bergh, F., Engelbretch, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3) (2004)Google Scholar
- 15.Wiegand, R.P., Liles, W.C., Jong, K.A.D.: An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings from the Genetic and Evolutionary Computation Conference, pp. 1235–1242. Morgan Kaufmann, San Francisco (2001)Google Scholar