UEGO, an abstract niching technique for global optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1498 LNCS, Issue , 1998, Pages 378-387

  Jelasity, Márk ^a  

^a JATE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

GLOBAL OPTIMIZATION;

NICHING TECHNIQUES; OPTIMIZERS; PARALLELIZING; SEARCH METHOD;

GENETIC ALGORITHMS;

EID: 84878609084     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0056880     Document Type: Conference Paper

References (13)

1. Thomas Bäck, editor. Proceedings of the Seventh International Conference on Genetic Algorithms, San Francisco, California, 1997. Morgan Kaufmann.
2. D. Beasley, D. R. Bull, and R. R. Martin. A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1(2):101-125, 1993.
3. K. Deb. Genetic algorithms in multimodal function optimization. TCGA report no. 89002, The University of Alabama, Dept. of Engineering mechanics, 1989.
4. K. Deb and David E. Goldberg. An investegation of niche and species formation in genetic function optimization. In J. D. Schaffer, editor, The Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, 1989.
5. A. E. Eiben and J. K. van der Hauw. Graph coloring with adaptive genetic algorithms. Journal of Heuristics, 4(1), 1998.
6. J. J. Grefenstette. Genesis: A system for using genetic search procedures. In Proceedings of the 1984 Conference on Intelligent Systems and Machines, pages 161-165, 1984.
7. Hisao Ishibuchi, Tadahiko Murata, and Shigemitsu Tomioka. Effectiveness of genetic local search algorithms. In Bäck [1], pages 505-512.
8. Mark Jelasity. A wave analysis of the subset sum problem. In Bäck [1], pages 89-96.
9. Márk Jelasity and József Dombi. GAS, a concept on modeling species in genetic algorithms. Artificial Intelligence, 99(1):1-19, 1998.
10. A. Juels and M. Wattenberg. Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Technical report, UC Berkeley, 1994.
11. S. Khuri, T. Bäck, and J. Heitkötter. An evolutionary approach to combinatorial optimization problems. In The Proceedings of CSC'94, 1993.
12. M. Mitchell, J. H. Holland, and S. Forrest. When will a genetic algorithm outperform hillclimbing? In J. D. Cowan et al., editors, Advances in Neural Information Processing Systems 6. Morgan Kaufmann, 1994.
13. Mutsunori Yagiura and Toshihide Ibaraki. Genetic and local search algorithms as robust and simple optimization tools. In Ibrahim H. Osman and James P. Kelly, editors, Meta-Heuristics: Theory and Application, pages 63-82. Kluwer Academic Publishers, 1996.