Abstract
This paper presents a component-based model with a novel ranking method (CMR) for constrained evolutionary optimization. In general, many constraint-handling techniques inevitably solve two important problems: (1) how to generate the feasible solutions, (2) how to direct the search to find the optimal feasible solution. For the first problem, this paper introduces a component-based model. The model is useful for exploiting valuable information from infeasible solutions and for transforming infeasible solutions into feasible ones. Furthermore, a new ranking strategy is designed for the second problem. The new algorithm is tested on several well-known benchmark functions, and the empirical results suggest that it continuously found the optimums in 30 runs and has better standard deviations for robustness and stability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Coello, C.A.C.: Theoretical and numerical constraint handling techniques used with evolutionary algorithms: A survey of the state of the art. J. Computer Methods in Applied Mech. 191(11-12), 1245–1287 (2002)
Kazarlis, S., Petridis, V.: Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms. J. Computer Science 1498, 211–220 (1998)
Farmani, R., Wright, J.A.: Self-adaptive fitness formulation for constrained optimization. J. IEEE Trans. Evolutionary Computation 7(5), 445–455 (2003)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. J. IEEE Trans. Evolutionary Computation 4(3), 284–294 (2000)
Koziel, S., Michalewicz, Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. J. Evolutionary Computation 7, 19–44 (1999)
Zhou, Y., Li, Y., He, J., Kang, L.: Multiobjective and MGG evolutionary algorithm for constrained optimization. In: IEEE Conference on Evolutionary Computation 2003, pp. 1–5. IEEE Press, Los Alamitos (2003)
Venkatraman, S., Yen, G.G.: A generic framework for constrained optimization using genetic algorithms. J. IEEE Trans. Evolutionary Computation 9(4), 424–435 (2005)
Runarsson, T.P., Yao, X.: Search biases in constrained evolutionary optimization. J. IEEE Trans. Evolutionary Computation 35(2), 233–243 (2005)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution algorithms. In: IEEE Conference on Evolutionary Computation 2006, pp. 6756–6763. IEEE Press, Canada (2006)
Mezura-Montes, E., Coello, C.A.C.: A simple multimembered evolution strategy to solve constrained optimization problems. J. IEEE Trans. Evolutionary Computation 9(1), 1–17 (2005)
Du, T., Fei, P., Shen, Y.: A Modified Niche Genetic Algorithm Based on Evolution Gradient and Its Simulation Analysis. In: Third International Conference on Natural Computation, China, vol. 4, pp. 35–39 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Y., Li, Y., Xu, X. (2009). A Novel Component-Based Model and Ranking Strategy in Constrained Evolutionary Optimization. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)