A rough penalty genetic algorithm for constrained optimization

Information Sciences

Volumn 241, Issue , 2013, Pages 119-137

  Lin, Chih Hao ^a  

^a CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Constrained optimization; Genetic algorithm; Penalty function; Rough set theory

Indexed keywords

CONSTRAINED OPTIMI-ZATION PROBLEMS; CROSSOVER OPERATIONS; INFEASIBLE SOLUTIONS; NOVEL GENETIC ALGORITHM; PENALTY FUNCTION; PERFORMANCE ANALYSIS; SELF-ADAPTIVE METHOD; STATE-OF-THE-ART ALGORITHMS;

CONSTRAINED OPTIMIZATION; ROUGH SET THEORY;

GENETIC ALGORITHMS;

EID: 84878253031     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.04.001     Document Type: Article

References (42)

1. H.J.C. Barbosa, and A.C.C. Lemonge A new adaptive penalty scheme for genetic algorithms Information Sciences 156 2003 215 251
2. N. Belavendram Quality by Design 1995 Prentice-Hall Englewood Cliffs, NJ
3. C.A.C. Coello Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Computer Methods in Applied Mechanics and Engineering 191 11-12 2002 1245 1287
4. K. Deb An efficient constraint handling method for genetic algorithms Computer Methods in Applied Mechanics and Engineering 186 2000 311 338
5. E.Z. Elfeky, R.A. Sarker, and D.L. Essam Analyzing the simple ranking and selection process for constrained evolutionary optimization Journal of Computer Science and Technology 23 1 2008 19 34
6. R. Farmani, and J.A. Wright Self-adaptive fitness formulation for constrained optimization IEEE Transactions on Evolutionary Computation 7 5 2003 445 455
7. M. Gen, and R. Cheng A survey of penalty techniques in genetic algorithms Evolutionary Computation 1996 804 809
8. A.B. Hadj-Alouane, and J.C. Bean A genetic algorithm for the multiple-choice integer program Operations Research 45 1 1997 92 101
9. A.G. Hernández-Díaz, L.V. Santana-Quintero, C.A.C. Coello, R. Caballero, J. Molina, A new proposal for multi-objective optimization using differential evolution and rough sets theory, in: Proc. Genetic and Evolutionary Computation Conference, 2006, pp. 675-682.
10. R. Hinterding, Gaussian mutation and self-adaptation for numeric genetic algorithms, in: Proc. IEEE Int. Conf. Evolutionary Computation, 1997, pp. 87-91.
11. P.Y. Ho, and K. Shimizu Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme Information Sciences 177 14 2007 2985 3004
12. W. Hock, and K. Schittkowski Test examples for nonlinear programming codes Lecture Notes in Economics and Mathematical Systems vol. 187 1981 Springer-Verlag New York
13. J.H. Holland Adaptation in Natural and Artificial Systems 1975 MIT Press
14. A. Homaifar, C. Qi, and S. Lai Constrained optimization via genetic algorithms Simulation 62 4 1994 242 254
15. J.A. Joines, C.R. Houck, On the use of nonstationary penalty functions to solve nonlinear constrained optimization problems with GA's, in: Proc. 1st IEEE Conf. Evolutionary Computation, 1994, pp. 579-584.
16. S.O. Kimbrough, G.J. Koehler, M. Lu, and D.H. Wood On a feasible-infeasible two-population (FI-2Pop) genetic algorithm for constrained optimization: distance tracing and no free lunch European Journal of Operational Research 190 2008 310 327
17. J. Komorowski, L. Polkowski, and A. Skowron Rough sets: a tutorial S.K. Pal, A. Skowron, Rough Fuzzy Hybridization: A New Trend in Decision-Making 1999 Springer-Verlag Singapore 3 98
18. S. Koziel, and Z. Michalewicz Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization Evolutionary Computation 7 1 1999 19 44
19. A.C.C. Lemonge, and H.J.C. Barbosa An adaptive penalty scheme for genetic algorithms in structural optimization International Journal for Numerical Methods in Engineering 59 5 2004 703 736
20. C-H. Lin, and C-C. Chuang A rough set penalty function for marriage selection in multiple-evaluation genetic algorithms Lecture Notes in Computer Science 4481 2007 500 507
21. C-H. Lin, J-D. He, A multiple-evaluation genetic algorithm for numerical optimization problems, in: Proc. Computability in Europe: Computation and Logic in the Real World, 2007, pp. 239-246.
22. Z. Michalewicz, A survey of constraint handling techniques in evolutionary computation methods, in: Proc. 4th Annual Conf. Evolutionary Programming, 1995, pp. 135-155.
23. Z. Michalewicz, and M. Schoenauer Evolutionary algorithms for constrained parameter optimization problems Evolutionary Computation 4 1996 1 32
24. Z. Pawlak Rough sets International Journal of Computer and Information Sciences 11 1982 341 356
25. Z. Pawlak, and A. Skowron Rudiments of rough sets Information Sciences 177 2007 3 27
26. Z. Pawlak, and A. Skowron Rough sets: some extensions Information Sciences 177 2007 28 40
27. Z. Pawlak, and A. Skowron Rough sets and Boolean reasoning Information Sciences 177 2007 41 73
28. D. Powell, M.M. Skolnick, Using genetic algorithms in engineering design optimization with non-linear constraints, in: Proc. 5th Int. Conf. Genetic Algorithms, 1993, pp. 424-431.
29. T.P. Runarsson, and X. Yao Stochastic ranking for constrained evolutionary optimization IEEE Transactions on Evolutionary Computation 4 2000 344 354
30. T.P. Runarsson, and X. Yao Search biases in constrained evolutionary optimization IEEE Transactions on Systems Man Cybernetics - Part C: Applications and Reviews 35 2 2005 233 243
31. L.V. Santana-Quintero, A.G. Hernández-DÍaz, J. Molina, C.A.C. Coello, and R. Caballero DEMORS: a hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems Computers & Operations Research 2009 10.1016/j.cor.2009.02.006
32. M. Schoenauer, S. Xanthakis, Constrained GA optimization, in: Proc. 5th Int. Conf. Genetic Algorithms, 1993, pp. 573-580.
33. G. Taguchi, S. Chowdhury, and S. Taguchi Robust Engineering 2000 McGraw-Hill New York
34. B. Tessema, G.G. Yen, A self-adaptive penalty function based algorithm for constrained optimization, in: Proc. IEEE Congress on Evolutionary Computation, 2006, pp. 950-957.
35. J.T. Tsai, T.K. Liu, and J.H. Chou Hybrid Taguchi-genetic algorithm for global numerical optimization IEEE Transactions on Evolutionary Computation 8 4 2004 365 377
36. S. Venkatraman, and G.G. Yen A generic framework for constrained optimization using genetic algorithms IEEE Transactions on Evolutionary Computation 9 4 2005 424 435
37. Y. Wang, Z. Cai, G. Guo, and Y. Zhou Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 37 3 2007 560 575
38. Y. Wang, Z. Cai, Y. Zhou, and Z. Fan Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique Structural and Multidisciplinary Optimization 37 4 2009 395 413
39. Y. Wang, Z. Cai, Y. Zhou, and W. Zeng An adaptive tradeoff model for constrained evolutionary optimization IEEE Transactions on Evolutionary Computation 12 1 2008 80 92
40. Y. Wu Taguchi Methods for Robust Design 2000 ASME New York
41. M. Yuchi, J.H. Kim, Grouping-based evolutionary algorithm: seeking balance between feasible and infeasible individuals of constrained optimization problems, in: Proc. Congress on Evolutionary Computation, 2004, pp. 280-287.
42. M. Zhang, W. Luo, and X. Wang Differential evolution with dynamic stochastic selection for constrained optimization Information Sciences 178 15 2008 3043 3074