A Cooperative Coevolutionary Approach to Function Optimization

A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets.

[1]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[4]  Richard H. Haunton.  Heart of the Valley: A History of Knoxville, Tennessee. Ed. by Lucile Deaderick. (Knoxville: East Tennessee Historical Society, 1976. xiii + 701 pp. Maps, illustrations, tables, appendixes, notes, and index. $15.00.) , 1977 .

[5]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[6]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[7]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[8]  Alan Bundy,et al.  Proceedings of the Eighth International Joint Conference on Artificial Intelligence , 1983 .

[9]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[10]  Dana S. Richards,et al.  Punctuated Equilibria: A Parallel Genetic Algorithm , 1987, ICGA.

[11]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[12]  John J. Greffenstette,et al.  A System for Learning Control Strategies with Genetic Algorithms , 1989 .

[13]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[14]  John J. Grefenstette,et al.  Incremental Learning of Control Strategies with Genetic algorithms , 1989, ML.

[15]  John J. Grefenstette,et al.  A System for Learning Control Strategies with Genetic Algorithms , 1989, ICGA.

[16]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[17]  L. Darrell Whitley,et al.  GENITOR II: a distributed genetic algorithm , 1990, J. Exp. Theor. Artif. Intell..

[18]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[19]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[20]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

[21]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[22]  Phil Husbands,et al.  Simulated Co-Evolution as the Mechanism for Emergent Planning and Scheduling , 1991, ICGA.

[23]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[24]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.