A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments

Soft Computing

Volumn 9, Issue 11, 2005, Pages 803-814

  Uyar, A Şima ^a   Harmanci, A Emre ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Adaptation; Diploid chromosomes; Diversity; Dominance change; Dynamic environments; Genetic algorithms; Population based adaptation

Indexed keywords

EID: 29544441800     PISSN: 14327643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00500-004-0421-4     Document Type: Article

References (26)

1. Baluja S, Caruana R (1995) Removing the genetics from the standard genetic algorithm. In: Proceedings of international conference on machine learning, Morgan Kaufmann, pp 38-46
2. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. Congress on evolutionary computation CEC99, IEEE, pp 1875-1882
3. Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer, Dordrecht
4. Branke J, Schmeck H (2002) Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui S, Ghosh A (Eds.) Theory and application of evolutionary computation: Recent trends Springer, Berlin Heidelberg New York, pp. 239-262
5. Cantu-Paz E (2002) On random numbers and the performance of genetic algorithms. Genetic and evolutionary computation conference GECCO'02. Morgan Kaufman, pp 311-318
6. Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent, nonstationary environments. Technical report AIC-90-001, Naval research Laboratory
7. Collingwood E, Corne D, Ross P (1996) Useful diversity via multiploidy. In: Proceedings of AISB workshop on evolutionary computation
8. Curtis H, Barnes NS (1981) Invitation to biology. 3rd edn. Worth, New York
9. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3(2):124-141 10.1109/ 4235.771166
10. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
11. Greene F (1996) A method for utilizing diploid/dominance in genetic search. In: Proceedings of the first IEEE conference on evolutionary computation
12. Grefenstette JJ (1992) Genetic algorithms for changing environments. Parallel problem solving from nature 2. North Holland, pp 137-144
13. Hadad BS, Eick CF (1997) Supporting polyploidy in genetic algorithms using dominance vectors. In: Proceedings of the sixth international conference on evolutionary programming, Vol 1213. Lecture Notes in Computer Science, Springer, Berlin Heidelberg New York
14. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
15. Lewis J, Hart E, Graeme R (1998) A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Proceedings of parallel problem solving from nature, Vol 1498. Lecture Notes in Computer Science, Springer, Berlin Heidelberg New York
16. Mori N, Kita H, Nishikawa Y (1996) Adaptation to a changing environment by means of the thermodynamical genetic algorithm. Parallel problem solving from nature, Vol 1141. Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York, pp 513-522
17. Ng KP, Wong KC (1995) A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the sixth international conference on genetic algorithms
18. Ryan C (1994) The degree of oneness. In: Proceedings of the 1994 ECAI workshop on genetic algorithms. Springer, Berlin Heidelberg New York
19. Smith RE, Goldberg DE (1992) Diploidy and dominance in artificial genetic search. Complex Syst 6:251-285
20. Uyar AS, Harmanci AE (1999) Investigation of new operators for a diploid genetic algorithm. Proceedings of SPIE: Applications and science of neural networks. Fuzzy systems and evolutionary computation II
21. Uyar AS, Harmanci AE (2002) Preserving diversity through diploidy and meiosis for improved GA performance in dynamic environments, Vol 2457. Lecture Notes in Computer Science, Springer, Berlin Heidelberg New York, pp 314-323
22. Uyar AS, Harmanci AE (2002) Performance comparisons of genotype to phenotype mapping schemes for diploid representations in changing environments. Recent advances in soft computing RASC'02
23. Uyar AS, Harmanci AE (2003) An adaptive domination map approach for multi-allelic diploid genetic algorithms. Genetic and evolutionary computation congress, GECCO 2003, Late breaking papers, pp 291-298
24. Uyar AS, Harmanci AE (2004) Comparison of domination approaches for diploid binary genetic algorithms. Applications and science in soft computing, Advances in soft computing. Springer, Berlin Heidelberg New York, pp 75-80
25. Uyar AS, Sariel S, Eryigit G (2004) A gene based adaptive mutation strategy for genetic algorithms. accepted for publication in genetic and evolutionary computation conference, GECCO-04. Springer, Berlin Heidelberg New York
26. Vavak F, Jukes K, Fogarty TC (1997) Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. Seventh international conference on evolutionary programming, Vol 1213. Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York, pp 719-726