Automated operator selection on genetic algorithms

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

Volumn 3684 LNAI, Issue , 2005, Pages 903-909

  Hilding, Fredrik G ^a   Ward, Koren ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER APPLICATIONS; PROBLEM SOLVING; SEARCH ENGINES;

MUTATION OPERATOR; NEAR OPTIMAL SOLUTIONS; SEARCH CAPACITY; SEARCH SPACE;

GENETIC ALGORITHMS;

EID: 33745312726     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11554028_126     Document Type: Conference Paper

References (7)

1. John Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The University of Michigan Press, second edition 1993 MIT press edition (1975)
2. David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing, MA (1989).
3. J. Mao; K. Hirasawa; J. Hu and J. Murata. Increasing robustness of genetic algorithm. In Publishers, M. K.,ed., Proceedings of the Genetic and Evolutinary Computation Conference (2002) 456-462
4. H. Suzuki, and Y. Iwasa,.Crossover accelerates evolution in GAs with a Babel-like tness landscape: Mathematical analyses. Evolutionary Computation Journal, (1999) 7(3):275-310.
5. Kanta Vekaria and Chris Clack. Selective crossover in genetic algorithms: An empirical study. Lecture Notes in Computer Science (1998) 1498:438-447.
6. Michle Sebag and Marc Schoenauer. Controlling crossover through inductive learning. In Hans-Paul Schwefel Yuval Davidor and Reinhard Manner, editors, Parallel Problem Solving from Nature - PPSN III, Springer (1994) 209-218.
7. T. Gerasch and P. Wang. A survey of parallel algorithm for one-dimensional integer knap-sack problems. INFOR (1993) 32(3):163-186.