Abstract
This paper introduces CCS, a Co-evolutionary approach to Constraint Satisfaction. Two types of objects — constraints and solutions — interact in a way modelled after predator and prey relations in nature. It is shown that co-evolution considerably focuses the genetic search. In addition, the new technique of life-time fitness evaluation (LTFE) is introduced. Its partial but continuous nature allows for efficient fitness evaluation. Moreover, co-evolution and LTFE nicely complement each other. Hence, their combined use further improves the performance of the evolutionary search.
CCS also provides a new perspective on the problems associated with high degrees of epistasis and the use of penalty functions.
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References
Davis, L., (1988), Applying Adaptive Algorithms to Epistatic Domains, Proc. IJCAI-88.
Hillis, W.D., (1992), Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure, in Artificial Life II, Langton, C.G.; Taylor, C.; Farmer, J.D., and Rasmussen, S., (eds), Addison-Wesley, California.
Mühlenbein, H., (1992), Parallel Genetic Algorithms in Combinatorial Optimization, Proc. Computer Science and Operations Research: New Developments in Their Interfaces, ORSA, Pergamon Press.
Paredis, J., (1993), Genetic State-Space Search for Constrained Optimization Problems, Proc. Thirteenth International Joint Conference on Artificial Intelligence (IJCAI 93), Morgan Kaufmann Publishers.
Paredis, J., (1994a), Steps towards Co-evolutionary Classification Neural Networks, Proc. Artificial Life IV, R. Brooks, P. Maes (eds), MIT Press / Bradford Books.
Paredis, J., (1994b), The Symbiotic Evolution of Solutions and their Representation, (in preparation).
Richardson, J.T.; Palmer, M.R.; Liepins, G.; Hilliard M., (1989), Some Guidelines for Genetic Algorithms with Penalty Functions. Proc. Third Int. Conf. on Genetic Algorithms, Morgan Kaufmann.
Whitley, D., (1989a), The Genitor Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trails is Best. Proc. Third Int. Conf. on Genetic Algorithms, Morgan Kaufmann.
Whitley, D., (1989b), Optimizing Neural Networks using Faster, more Accurate Genetic Search. Proc. Third Int. Conf. on Genetic Algorithms, Morgan Kaufmann.
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© 1994 Springer-Verlag Berlin Heidelberg
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Paredis, J. (1994). Co-evolutionary constraint satisfaction. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_249
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DOI: https://doi.org/10.1007/3-540-58484-6_249
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