Parameters control in GAs for dynamic optimization

International Journal of Computational Intelligence Systems

Volumn 6, Issue 1, 2013, Pages 47-63

  Jebari, Khalid ^a   Bouroumi, Abdelaziz ^a,b   Ettouhami, Aziz ^a  

^a FACULTÉ DES SCIENCES   (Morocco)

^b FACULTÉ DES SCIENCES BEN M SIK   (Morocco)

Author keywords

Dynamic Optimization; Evolutionary Algorithms; Fitness Sharing; Fuzzy Clustering; Generalized dynamic benchmark generator (GDBG); Genetic Algorithms; Multi modal function optimization; Unsupervised Learning

Indexed keywords

ALGORITHM PARAMETERS; DIVERSITY MEASURE; DYNAMIC OPTIMIZATION; DYNAMIC OPTIMIZATION PROBLEM (DOP); FITNESS SHARING; FUZZY CLUSTERING METHOD; GAS PARAMETERS; GENERALIZED DYNAMIC BENCHMARK GENERATOR (GDBG); GENETIC OPERATORS; MULTIMODAL FUNCTION OPTIMIZATION; MULTIPLE CROSSOVER; NON-UNIFORM MUTATION; NOVEL TECHNIQUES; SEARCH PROCESS; SEARCH SPACES; SELECTION PRESSURES; SELF-ADAPTIVE MUTATION; STATIC OPTIMIZATION;

EVOLUTIONARY ALGORITHMS; FUZZY CLUSTERING; GENETIC ALGORITHMS; OPTIMIZATION; UNSUPERVISED LEARNING;

PARAMETER ESTIMATION;

EID: 84872420102     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2013.754172     Document Type: Article

References (37)

1. Jin, Y. and Branke, J. 2005. Evolutionary optimization in uncertain environments: a survey. IEEE Trans Evol Comput, 9(3): 303-317.
2. J. Branke, E. Salihoglu, and S. Uyar, "Towards an analysis of dynamic environments", Genetic and Evolutionary Computation Conference, GECCO 2005, ACM 2005, 1433 1440, 2005.
3. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York 1981
4. J. Branke, Evolutionary Optimization in Dynamic Environments, 2002 Kluwer Academic Publishers, Dordrecht.
5. S. Yang, X. Yao, "Population-based incremental learning with associative memory for dynamic environments", IEEE Trans Evol Comput. 2007
6. J. Branke, T. Kau, L. Schmidt and H. Schmeck, "A multi-population approach to dynamic optimization problems", In: 4th International Conference on Adaptive Computing in Design and Manufacture ACDM 2000 2000.
7. Bouroumi, A. and Essaïdi, A. 2000. Unsupervised fuzzy learning and cluster seeking. Intelligent Data Analysis, 4(3): 241-253.
8. Yang, S. and Yao, X. 2008. Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput, 12(5): 542-561.
9. Cedeno, W. and Vemuri, V. R. 2007. A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines, 8(3): 255-286.
10. H.G. Cobb An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments, Technical Report 6760 (NLR Memorandum)., Washington, D.C1990.
11. Deb, K. and Kumar, A. 1995. Real-coded Genetic Algorhithms with Simulated Binary Crossover:Studies on Multimodel and Multiobjective Problems. Complex Systems, 9(6): 431-454.
12. Deb, K. and Agrawal, R.B. 1995. Simulated binary crossover for continuous search space. Complex systems, 9(2): 115-148.
13. Eiben, A.E. and Smit, S. K. 2011. Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1): 19-31.
14. Tanga, P.H. and Tsenga, M.H. 2013. Adaptive directed mutation for real-coded genetic algorithms. Applied Soft Computing, 13(1): 600-614.
15. D.E. Goldberg, and Wang L " Adaptative niching via coevolutionary sharing ", Genetic algorithms in engineering and computer science Wiley, Chichester1997, pp 21-38.
16. A. Agrawal, and I. Mitchell, Selection enthusiasm, Proceedings of the 6th international conference on Simulated Evolution And Learning, Springer-Verlag, pp 449-456 2006.
17. Grefenstette, J.J. 2007. A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput, 4(3): 243-254.
18. Jebari, K., Bouroumi, A.Ettouhami, A. 2011. Unsupervised fuzzy tournament selection. Applied Mathematical Sciences, 5(58): 2863-2881.
19. Kuncheva, L.I. and Bezdek, J.C. 1998. Nearest prototype classification: clustering, genetic algorithms or random search. IEEE Trans. Systems Man Cybernet, C 28(1): 160-164.
20. C. Li and S. Yang " A generalized approach to construct benchmark problems for dynamic optimization ", Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, 2008 Springer.
21. C. Li, S. Yang, T.T. Nguyen, H. Jin, G. Beyer,and P.N. Sugantha n, Benchmark Generator for CEC'2009 Competition on Dynamic Optimization, Technical Report 2008, Department of Computer Science, University of Leicester, U.K 2008.
22. S.J. Louis, Z. Xu, " An immigrants scheme based on environmental information for genetic algorithms in changing environments ", In: Proceedings of the 2008 IEEE congress on evolutionary computation, 1141-1147, 2008.
23. Michalewicz, Z. 1996. Genetic algorithms + data structures = evolution programs, London, UK: Springer-Verlag.
24. Kondrashov, A. S and Crow, J. F. 1994. Haploidy or Diploidy: Which is Better?. Nature, 351: 314-315.
25. Pelikan, M. and Goldberg, D.E. 2001. "Escaping hierarchical traps with competent genetic algorithms". In Proceedings of the Genetic and Evolutionary Computation Conference 511-518.
26. Saäreni, B. and Krähenbühl, L. 1998. Fitness sharing and niching methods revisited. IEEE Trans. Evol. Comput, 2(3): 97-106.
27. R.K. Ursem, Multinational GAs: Multimodal optimization techniques in dynamic environments, In: Proc. of (GECCO'2000), Morgan Kaufmann, San Francisco, 19-26 2000.
28. Yang, S. 2007. Genetic algorithms with elitism-based immigrants for changing optimization problems. Applications of Evolutionary Computing LNCS, 4448: 627-636.
29. S. Yang, Memory-based immigrants for genetic algorithms in dynamic environment, Proc. of (GECCO'05) ACM Press, New York, 1115-1122 2005.
30. K. deb, and D. Goldberg, An investigation of niche and species formation in genetic function optimization,:In: Schaffer Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, 42-50 1989.
31. Bui, L.T., Abbass, H.A. and Branke, J. 2005. Multiobjective optimization for dynamic environments. in Proc. Congr. Evol. Comput., 3: 2349-2356.
32. Trojanowski, K and Wierzchon, S. 2008. Immune-based algorithms for dynamic optimization. Applied Soft Computing, 8(2): 1495-1515.
33. Eiben, A., Michalewicz, Z., Schoenauer, M. and Smith, J. 2007. "Parameter control in evolutionary algorithms". In Parameter Setting in Evolutionary Algorithms, Vol. 54 of Studies in Computational Intelligence, Edited by: Lobo, F. G., Lima, C. F. and Michalewicz, Z. Vol. 54, 19-46. Berlin: Springer Verlag.
34. Serpell, M. and Smith, J.E. 2010. Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms. Evolutionary Computation MIT Press, 18(3): 491-514.
35. Boyabalti, O. and Sabuncuoglu, I. 2007. Parameter Selection in Genetic Algorithms. System, Cybernatics and Informatics, 2(4): 78-83.
36. M. Smetek, and B. Trawiński, Investigation of genetic algorithms with self-adaptive crossover, mutation, and selection, Hybrid Artificial Intelligent Systems, 116 123, 2011.
37. Zhang, J., Chung, H.S.H. and Lo, W.L. 2007. Clustering based adaptive crossover and mutation probabilities for genetic algorithms. Evolutionary Computation, IEEE Transactions., 11(3): 326-335.