The minimal deceptive problem revisited: The role of "genetic waste"

Computers and Operations Research

Volumn 25, Issue 11, 1998, Pages 895-911

  Novkovic, Sonja ^a   Šverko, Davor ^b  

^a SAINT MARY S UNIVERSITY   (Canada)

^b Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Control parameters; Genetic algorithm; Minimal deceptive problem; Nonsense codon

Indexed keywords

COMPUTER SIMULATION; OPTIMIZATION; RANDOM PROCESSES;

GOLDBERG'S MINIMAL DECEPTIVE PROBLEM; NONSENSE CODONS;

GENETIC ALGORITHMS;

EID: 0032210587     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0305-0548(98)00024-0     Document Type: Article

References (23)

1. Arifovic, J., Genetic algorithm learning and the Cobweb model. Journal of Economic Dynamics and Control, 1994, 18, 3-28.
2. Berg, P. and Singer, M., Dealing With Genes. University Science Books, Mill Valey, CA, 1992.
3. Booker, L., Improving search in genetic algorithms, In Genetic Algorithms and Simulated Annealing, ed. L. D. Davis. Morgan Kauffman, Los Altos, CA, 1987.
4. Davis, L., ed., Genetic Algorithms and Simulated Annealing. Pitman, London, 1987.
5. Davis, L., ed., Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
6. Dawid, H., Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models; Lecture Notes in Economics and Mathematical Systems 441. Springer-Verlag Berlin Heidelberg, 1996.
7. De Jong, K. A., Adaptive system design: a genetic approach. IEEE Transactions on Systems Man and Cybernetics, 1980, 10(9), 566-574.
8. De Jong, K. A., An analysis of the behavior of a class of genetic adaptive systems. Ph.D. dissertation, University of Michigan, 1975.
9. Dorsey, R. E. and Mayer, W. J., Genetic algorithms for estimation problems with multiple optima, non-differentiability and other irregular features. Journal of Business and Economic Statistics, 1995, 13(1), 53-66.
10. Goldberg, D. E., Simple genetic algorithm and the minimal, deceptive problem. In Genetic Algorithm and Simulated Annealing, ed. L. Davis. Pitman, London, 1987.
11. Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Co., 1989.
12. Grefenstette, J. J., Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1986, 16(1), 122-128.
13. Holland, J. H., Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.
14. Holland, J. H. and Miller, J. H., Artificial adaptive agents in eonomic theory. American Economic Review, 1991, 81(2), 365-370.
15. Novkovic, S., A genetic algorithm simulation of a transition economy: an application to insider-privatization in Croatia. Computational Economics, 1997, 7(4), 1-23.
16. Novkovic, S. and Sverko, D., Genetic waste and the role of diversity in genetic algorithm simulations. Paper presented at the Second Workshop on Economics with Heterogeneous Interacting Agents, Ancona, May 30-31, 1997.
17. Song, Y. H., Wang, G. S., Johns, A. T. and Wang, P. Y., Improved genetic algorithms with fuzzy logic controlled crossover and mutation, UKACC International Conference on Control, September 2-5. Conference Publication, 1996, 427, 140-144.
18. Srinivas, M. and Patnaik, L. M., Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(4), 656-667.
19. Szekely, M., From DNA to Protein: The Transfer of Genetic Information. John Willey and Sons, NY, 1980.
20. Venkatachalam, A. R., An analysis of an embedded crossover scheme on a GA-hard problem. Computers and Operations Research, 1995, 22(1), 149-157.
21. Wu, A. S. and Lindsay, R. K., Empirical studies of the genetic algorithm with non-coding segments. Evolutionary Computation, 1995, 3(2), 121-147.
22. th IEEE International Conference on Tools With Artificial Intelligence, New Orleans, 1994.
23. Wu, Q. H. and Cao, Y. J., Stochastic Optimization of Control Parameters in Genetic Algorithms, 1997, IEEE, 5, 77-80.