Abstract
Penalty functions are often used to handle constrained optimization problems in evolutionary algorithms. However, most of the penalty adjustment methods are based on mathematical approaches not on evolutionary ones. To mimic the biological phenomenon of the values judgment, we introduce the rough set theory as a novel penalty adjustment method. Furthermore, a new marriage selection is proposed in this paper to modify the multiple-evaluation genetic algorithm. By applying rough-penalty and marriage-selection methods, the proposed algorithm generally is both effective and efficient in solving several constrained optimization problems. The experimental results also show that the proposed mechanisms further improve and stabilize the solution ability.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1975)
Gen, M., Cheng, R.: A Survey of Penalty Techniques in Genetic Algorithms. Evol. Comput., 804–809 (1996)
Homaifar, A., Qi, C.X., Lai, S.H.: Constrained Optimization via Genetic Algorithms. Simulation, 242–254 (1994)
Joines, J.A., Houck, C.R.: On the Use of Nonstationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with GA’s. In: Proc. 1st IEEE Conf. Evolutionary Computation, pp. 579–584 (1994)
Hadj-Alouane, A.B., Bean, J.C.: A Genetic Algorithm for the Multiple-choice Integer Program. Operations Research, 92–101 (1997)
Lin, C.H., He, J.D.: A Multiple-Evaluation Genetic Algorithm for Numerical Optimization Problems. In: Proc. Computability in Europe: Computation and Logic in the Real World (2007)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Li, F., et al.: A New Crossover Operator Based on the Rough Set Theory for Genetic Algorithms. In: Proc. of 4th International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 18–21 (2005)
Michalewicz, Z., Schoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evol. Comput 4, 1–32 (1996)
Runarsson, T., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Trans. Evol. Comput. 4, 344–354 (2000)
Farmani, R., Wright, J.: Self-adaptive Fitness Formulation for Constrained Optimization. IEEE Trans. Evol. Comput. 7(5), 445–455 (2003)
Venkatraman, S., Yen, G.G.: A Generic Framework for Constrained Optimization Using Genetic Algorithms. IEEE Trans. on evolutionary computation 9(4), 424–435 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Lin, CH., Chuang, CC. (2007). A Rough Set Penalty Function for Marriage Selection in Multiple-Evaluation Genetic Algorithms. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_62
Download citation
DOI: https://doi.org/10.1007/978-3-540-72458-2_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
eBook Packages: Computer ScienceComputer Science (R0)