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An adaptive evolutionary algorithm for the satisfiability problem

Authors:

Claudio Rossi,

Elena Marchiori,

Joost N. KokAuthors Info & Claims

SAC '00: Proceedings of the 2000 ACM symposium on Applied computing - Volume 1

Pages 463 - 469

https://doi.org/10.1145/335603.335912

Published: 19 March 2000 Publication History

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References

[1]

T. Back, A. Eiben, and M. Vink. A superior evolutionary algorithm for 3-SAT. In D. W. N. Saravanau and A. Eiben, editors, Proceedings of the 7th Annual Confercnce on Evolutionary Programming, Lecture Notes in Comput. er Science~ pages 125-136. Springer. 1998.

Digital Library

Google Scholar

[2]

R. Battiti and M. Protasi. Approximate algorithms and heuristics lor MAX-SAT. In D.-Z. Du and P. Pardslos, editors, Handbook of Combinatorial Optimization, pagcs 77-148. Kluver Academic Publisher, 1998.

Google Scholar

[3]

D. Du, J. Gu, and P. P. (Eds.). Satisfiability Problem: Theory and Applications. AMS, DIMACS Series in Dis~ crete Mat:hematics and Theoretical Computer Science, vol 35, 1997.

Google Scholar

[4]

A. Eibcil and J. van der Hauw. Solving 3-SAT with adaptive Genetic AlgorittlnLs. In Proceedings of the,$th IEEE Conference o~ Evolutionary Computation~ pages 81~86. IEEE Press, 1997.

Google Scholar

[5]

G.Folino, C.Pizzuti, and G.Spezzano. Combining colinlax genetic algorithms and local search for solving satisfiability problems. In Proc. of ICTA.r'98 lOth IEEE International Conference Tools udth Artificial Intelligence, page~s 192-198. IEEE Computer Society, 1998.

Google Scholar

[6]

F. Glover and D. D. Werra. Tabu Search. Vol.41, Baltzer Science, 1993.

Google Scholar

[7]

J. Gottlieb and N. Voss. Improving the performance of evolutionary algorithms for the satisfiability problem by refining functions. In A. Eiben, T. Bck, M. Schoenauer, and H.-P. $chwefel, editors, Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature (PPSN V), LNGS 1498, pages 755- 764. Springer, 1998.

Digital Library

Google Scholar

[8]

E. Maxchiori. A simple heuristic based genetic algorithm for the maximum clique problem. In J. C. et al., editor, A CM Symposium on Applied Computing, pages 366-373. ACM Press, 1998.

Digital Library

Google Scholar

[9]

E. Maxchiori and C. Rossi. A flipping genetic algorithm for hard 3-SAT problems. In Genetic and Evolutionary Computation Conference, 1999.

Google Scholar

[10]

P. Merz and B. F~eisleben. Genetic local search for the TSP: New results. In IEEE International Conference on Evolutionawy Computation, pages 159-164. IEEE Press: 1997.

Crossref

Google Scholar

[11]

M. kesertde ~nd T. F~o. A GRASP for satisfiability, la D. Johnson and M. Trick~ editors, Cliques, Coloring and Satisfiability, pages 499-520. AMS, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol 26, 1996.

Google Scholar

[12]

B. Selman, H. Levesqne, and D. Mitchell. A new method for solving hard satisfiabitity problems. In Proceedings of the l Oth National Conference on Artificial Intelligence, AAAI-g2, pages 440-446. AAAI Press/The MIT Press, 1992.

Google Scholar

Cited By

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Hao JSolnon C(2020)Meta-heuristics and Artificial IntelligenceA Guided Tour of Artificial Intelligence Research10.1007/978-3-030-06167-8_2(27-52)Online publication date: 8-May-2020
https://doi.org/10.1007/978-3-030-06167-8_2
van Hemert J(2015)Evolutionary Computation and Constraint SatisfactionSpringer Handbook of Computational Intelligence10.1007/978-3-662-43505-2_65(1271-1288)Online publication date: 2015
https://doi.org/10.1007/978-3-662-43505-2_65
Tseng LChen C(2014)Solving the maximum satisfiability problem by fuzzy converting it into a continuous optimization problem2014 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2014.7009141(352-358)Online publication date: Jul-2014
https://doi.org/10.1109/ICMLC.2014.7009141
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An adaptive evolutionary algorithm for the satisfiability problem

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Published In

SAC '00: Proceedings of the 2000 ACM symposium on Applied computing - Volume 1

March 2000

536 pages

ISBN:1581132409

DOI:10.1145/335603

Chairman:
Barrett Bryant
Univ. of Alabama—Birmingham, Birmingham
,
Editors:
Janice Carroll
Rogers State Univ.
,
Ernesto Damiani
Univ. di Milano, Crema, Italy
,
Hisham Haddad
Marshall Univ., Huntington, WV
,
Dave Oppenheim

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 March 2000

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Cited By

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Hao JSolnon C(2020)Meta-heuristics and Artificial IntelligenceA Guided Tour of Artificial Intelligence Research10.1007/978-3-030-06167-8_2(27-52)Online publication date: 8-May-2020
https://doi.org/10.1007/978-3-030-06167-8_2
van Hemert J(2015)Evolutionary Computation and Constraint SatisfactionSpringer Handbook of Computational Intelligence10.1007/978-3-662-43505-2_65(1271-1288)Online publication date: 2015
https://doi.org/10.1007/978-3-662-43505-2_65
Tseng LChen C(2014)Solving the maximum satisfiability problem by fuzzy converting it into a continuous optimization problem2014 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2014.7009141(352-358)Online publication date: Jul-2014
https://doi.org/10.1109/ICMLC.2014.7009141
Bresolin DJiménez FSánchez GSciavicco GNeumann FDe Jong K(2013)Finite satisfiability of propositional interval logic formulas with multi-objective evolutionary algorithmsProceedings of the twelfth workshop on Foundations of genetic algorithms XII10.1145/2460239.2460243(25-36)Online publication date: 16-Jan-2013
https://dl.acm.org/doi/10.1145/2460239.2460243
Tseng LLin Y(2013)A hybrid genetic algorithm for the maximum satisfiability problemProceedings of the 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems10.1007/978-3-642-38577-3_12(112-120)Online publication date: 17-Jun-2013
https://dl.acm.org/doi/10.1007/978-3-642-38577-3_12
Takaki MCavalcanti DGheyi RIyoda JD'Amorim MPrudêncio R(2010)Randomized constraint solversInnovations in Systems and Software Engineering10.1007/s11334-010-0124-16:3(243-253)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1007/s11334-010-0124-1
Maturana JLardeux FSaubion F(2010)Autonomous operator management for evolutionary algorithmsJournal of Heuristics10.1007/s10732-010-9125-316:6(881-909)Online publication date: 9-Mar-2010
https://doi.org/10.1007/s10732-010-9125-3
Muñoz JGutierrez GSanchis A(2009)Evolutionary techniques in a constraint satisfaction problemProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689996(2985-2991)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689996
Munoz JGutierrez GSanchis A(2009)Evolutionary techniques in a constraint satisfaction problem: Puzzle Eternity II2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983319(2985-2991)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983319
Guo PLuo WLi ZLiang HWang X(2009)Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability ProblemsProceedings of the 4th International Symposium on Advances in Computation and Intelligence10.1007/978-3-642-04843-2_27(248-257)Online publication date: 1-Oct-2009
https://dl.acm.org/doi/10.1007/978-3-642-04843-2_27
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