Multiple Populations Guided by the Constraint-Graph for CSP
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In this paper we examine the gain of the performance obtained using multiple populations - that evolve in parallel - of the constraintgraph based evolutionary algorithm (in its dynamic adaptation operators version) with a migration policy. We show that a multiple populations approach outperforms a single population implementation when applying it to the 3-coloring problem. We also evaluate various migration policies.
- Adamis, Review of parallel genetic algorithms, Dept. Elect. Comp. Eng, Aristitele Univ. Thessaloniki, Greece, Tech. Rep. 1994.
- A. Beguelin, J. J. Dongarra, G. A. Geist, R. Manchek, and V. S. Sunderam, A Users’ Guide to PVM Parallel Virtual Machine, Oak Ridge National Laboratory, ORNL/TM-12187, September, 1994
- Brelaz, New methods to color vertices of a graph. Communications of the ACM, 22,pp. 251–256, 1979. CrossRef
- Cheeseman P., Kanefsky B., Taylor W., Where the Really Hard Problems Are. Proceedings of IJCAI-91, pp. 163–169, 1991
- Culberson, J. http://web.cs.ualberta.ca/ joe/
- G. Dozier, J. Bowen, and Homaifar, “Solving Constraint Satisfaction Problems Using Hybrid Evolutionary Search,” IEEE Transactions on Evolutionary Computation, Vol. 2, No. 1, 1998.
- A. E. Eiben, J. I. van Hemert, E. Marchiori, A.G. Steenbeek. Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function. Fifth International Conference on Parallel Problem Solving from Nature ( PPSN-V), LNCS 1498, pp. 196–205, 1998.
- Kumar. Algorithms for constraint satisfaction problems:a survey. AI Magazine, 13(1):32–44, 1992.
- Mackworth A.K., Consistency in network of relations. Artificial Intelligence, 8:99–118, 1977. CrossRef
- J. Paredis, Coevolutionary Algorithms, The Handbook of Evolutionary Computation, 1st supplement, BSck, T., Fogel, D., Michalewicz, Z. (eds.), Oxford University Press.
- Riff M.-C., From Quasi-solutions to Solution: An Evolutionary Algorithm to Solve CSP. Constraint Processing (CP96), Ed. Eugene Freuder, pp. 367–381, 1996.
- Riff M.-C., Evolutionary Search guided by the Constraint Network to solve CSP. Proc. of the Fourth IEEE Conf on Evolutionary Computation, Indianopolis, pp. 337–342, 1997.
- Riff M.-C., A network-based adaptive evolutionary algorithm for CSP, In the book “Metaheuristics: Advances and Trends in Local Search Paradigms for Optimisation”, Kluwer Academic Publisher, Chapter 22, pp. 325–339, 1998.
- Tsang, E. P. K., Wang, C. J., Davenport, A., Voudouris, C., Lau, T. L., A family of stochastic methods for constraint satisfaction and optimization, PACLP’99, London, pp. 359–383, 1999
- Multiple Populations Guided by the Constraint-Graph for CSP
- Book Title
- Advances in Artificial Intelligence
- Book Subtitle
- International Joint Conference 7th Ibero-American Conference on AI 15th Brazilian Symposium on AI IBERAMIA-SBIA 2000 Atibaia, SP, Brazil, November 19–22, 2000 Proceedings
- pp 457-466
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
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- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Department of Computer Science and Statistics Computational Intelligence Laboratory, University of São Paulo
- 2. Computer Engineering Department Intelligent Techniques Laboratory, University of São Paulo
- Author Affiliations
- 5. Computer Science Department, Universidad Técnica Federico Santa María, Valparaíso, Chile
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