A Rough Set Penalty Function for Marriage Selection in Multiple-Evaluation Genetic Algorithms

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.

[1]  James C. Bean,et al.  A Genetic Algorithm for the Multiple-Choice Integer Program , 1997, Oper. Res..

[2]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[3]  Mitsuo Gen,et al.  A survey of penalty techniques in genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[5]  Yao Lu,et al.  An Efficient Real-Coded Genetic Algorithm for Numerical Optimization Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[6]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  Fan Min,et al.  A new crossover operator based on the rough set theory for genetic algorithms , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Gary G. Yen,et al.  A generic framework for constrained optimization using genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[10]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[11]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[12]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.