An alternative model of genetic algorithms as learning machines

Expert Systems with Applications

Volumn 15, Issue 3-4, 1998, Pages 351-356

  Kuri, Angel ^a  

^a INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0346226035     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0957-4174(98)00042-6     Document Type: Article

References (13)

1. Anand, V., & Spears, W. (1991). A study of crossover operators in genetic programming. In Z. W. Ras, & M. Zemankova, Proceedings of the international symposium on methodologies for intelligent systems (pp. 409-418). Berlin: Springer-Verlag.
2. Beckman, F.S. (1981). Mathematical foundations of programming. Addison-Wesley.
3. De Jong, K., & Spears, W. (1995). An analysis of the interacting roles of population size and crossover in genetic algorithms. Available on-line: kdejong@aic.gmu.edu.
4. Goldberg, D. (1989a). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
5. Goldberg, D. (1989b). Genetic algorithms in search, optimization and machine learning (pp. 46-52). Addison-Wesley.
6. Goldberg D.E., Deb K., & Clark J. (1993). Genetic algorithms, noise and the sizing of populations. Complex Systems, 6, 333-362.
7. Holland J.H. (1962). Outline for a logical theory of adaptive systems. Journal of the Association for Computing Machinery, 3, 297-314.
8. Holland, J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
9. Kuri, A. (1996). The Vasconcelos model for crossover and selection as a viable alternative in genetic algorithms. Available on-line: akuri@pollux.cic.ipn.mx.
10. Mitchell, M. (1996). An introduction to genetic algorithms. Complex Adaptive System Series, MIT Press.
11. Spears, W. (1994). Adapting crossover in evolutionary algorithms. Artificial Intelligence Center internal report AIC-94-019. Washington, DC: Naval Research Laboratory.
12. Twardowski, K. (1994). An associative architecture for genetic algorithm-based machine learning. Computer, Nov., 34-39.
13. Wilcox, J. (1995). Organizational learning within a learning classifier system. IlliGAL report no. 95003. University of Illinois at Urbana-Champaign.