A genetic local search algorithm for random binary constraint satisfaction problems

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A genetic local search algorithm for random binary constraint satisfaction problems

Full text

Pdf (449 KB)

Source

Symposium on Applied Computing archive
Proceedings of the 2000 ACM symposium on Applied computing - Volume 1 table of contents

Como, Italy

Pages: 458 - 462

Year of Publication: 2000

ISBN:1-58113-240-9

Authors

Elena Marchiori	Faculty of Sciences, Free University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
Adri Steenbeek	CWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 21, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/335603.335910 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	N. Barnier and P. Brisset. Optimization by hybridation of a genetic algorithm with constraint satisfaction techniques. In international Conference on Evolutionary Computation, pages 645-649. IEEE, 1998.
	2	James Bowen , Gerry Dozier, Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit, Proceedings of the 6th International Conference on Genetic Algorithms, p.122-129, July 15-19, 1995
	3	B. Craenen, A. F, iben, and E. Mardliori. Solving constraint satisfaction problems with heuristic-based evolutionary algorithms. Ill Eleventh Belgium-Netherlarzds Conference. on Artifciat Intelligence, 1999.
	4	G. Dozier, J. Bowen, and D. Bahler. Solving small and large constraint satisfaction problems using a heuristic-based mL crogenetic algorithms. In Proceedings of the 1st {EEE Confete. nee on L%otutionary Computation, pages 306-311. IEEE Press, 1994.
	5	G. Dozier, J. t~owen, and A. Homaifar. Solving constraint satisfaction problems using hybrid evoJutionary search. IEEE Transactions on Evolutionary Computation, 2(1):23- 33, 1998.
	6	A.E. giben, P.-E. RatiO, and Zs. kuttkay. Constrained problems. In L. Chambers, editor, Practical Handbook of Genetic Algorithms, pages 307-365. CRC Press, 1995.
	7	A.E. Ethan and Zs. Ruttkay. Self-adaptivity for constraint satisfaction: Learning penalty functions. In Proceedings of the 3rd IEEE Conference on Evolutionary Computation, pages 258-261. IEEE Press, 1996.
	8	A. E. Eiben , Jano I. van Hemert , Elena Marchiori , A. G. Steenbeek, Solving Binary Constraint Satisfaction Problems Using Evolutionary Algorithms with an Adaptive Fitness Function, Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, p.201-210, September 27-30, 1998
	9	David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
	10	Kenneth Alan De Jong, An analysis of the behavior of a class of genetic adaptive systems., 1975
	11	Antoon Kolen , Erwin Pesch, Genetic local search in combinatorial optimization, Discrete Applied Mathematics, v.48 n.3, p.273-284, Feb. 15, 1994
	12	Elena Marchiori, A simple heuristic based genetic algorithm for the maximum clique problem, Proceedings of the 1998 ACM symposium on Applied Computing, p.366-373, February 27-March 01, 1998, Atlanta, Georgia, United States [doi>10.1145/330560.330841]
	13	P. Merz and B. Freisleben. Genetic local search for the tsp: New results. In IEEE International Conference on Evolutionary Computation, pages 159-164. IEEE Press, 1997.
	14	Zbigniew Michalewicz, Genetic AlgorithmsNumerical Optimizationand Constraints, Proceedings of the 6th International Conference on Genetic Algorithms, p.151-158, July 15-19, 1995
	15	P. Morris. The breakout method for escaping from local minima. In Proceedings of the 11 th National Conference on Artifetal Intelligence, AAAt-9S, pages 40-45. AAAI Press/The MIT Press, 1993.
	16	P. Mosc~to. On evolution, search, optimization, genetic algorithms and martial arts: Towards naemetic algorithms. Technical report, Caltech Concurrent Computation Program, Californian Institute of Technology, U.S.A., TR No790 1989.
	17	H. Miihlenbein, M. Gorges-Schleuter, and O. Kri~mer. Evolution algorithms in combinatorial optimization. Parallel Computing, 7:65-85, 1988.
	18	P. Prosser. An empirical study of pl, ase transitions in binary constraint satisfaction problems. Artificial Intelligence, 81:81-109, 1996.
	19	M.C. Riff-Rojas. Evolutionary search guided by the constraint network to solve CSP. In Proceedings of the dth IEEE Conference on Evolutionary Computation, pages 337-348. IEEE Press, 1997.
	20	M.C, Rift Rojas. Using the knowledge of the constraints network to design an evolutionary algorithm that solves CSP. In Proceedings of the 3rd IEEE Conference on Evolutionary Computation, pages 279-284. IEEE Press, 1996.
	21	Alvaro Ruiz-Andino , José J. Ruz, Integration of Constraint Programming and Evolution Programs: Application to Channel Routing, Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems, p.448-459, June 01-04, 1998
	22	B.M. Smith. Phase transition and the mushy region in constraint satisfaction problems. In A.G. Cohn, editor, Proceedings of the llth European Conference on Artificial Intelligence, pages 100-104. John Wiley & Sons Ltd., Aug. 1994.
	23	Colin P. Williams , Tad Hogg, Exploiting the deep structure of constraint problems, Artificial Intelligence, v.70 n.1-2, p.73-117, Oct. 1994 [doi>10.1016/0004-3702(94)90104-X]