research-article

pEvoSAT: a novel permutation based genetic algorithm for solving the boolean satisfiability problem

Authors:

Kay C. WieseAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 861 - 868

https://doi.org/10.1145/2463372.2463479

Published: 06 July 2013 Publication History

Abstract

In this paper we introduce pEvoSAT, a permutation based Genetic Algorithm (GA), designed to solve the boolean satisfiability (SAT) problem when it is presented in the conjunctive normal form (CNF). The use of permutation based representation allows the algorithm to take advantage of domain specific knowledge such as unit propagation, and pruning. In this paper, we explore and characterize the behavior of our algorithm. This paper also presents the comparison of pEvoSAT to GASAT, a leading implementation of GAs for the solving of CNF instances.

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Cited By

Cicirello V(2022)Cycle Mutation: Evolving Permutations via Cycle InductionApplied Sciences10.3390/app1211550612:11(5506)Online publication date: 29-May-2022
https://doi.org/10.3390/app12115506
Ali HEjaz WTaei MIqbal F(2019)Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON.2019.8936281(1092-1097)Online publication date: Oct-2019
https://doi.org/10.1109/IEMCON.2019.8936281
Raschip MCroitoru CFrasinaru C(2018)New Evolutionary Approaches for SAT Solving2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00086(522-526)Online publication date: Nov-2018
https://doi.org/10.1109/ICTAI.2018.00086
Show More Cited By

Index Terms

pEvoSAT: a novel permutation based genetic algorithm for solving the boolean satisfiability problem
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Cicirello V(2022)Cycle Mutation: Evolving Permutations via Cycle InductionApplied Sciences10.3390/app1211550612:11(5506)Online publication date: 29-May-2022
https://doi.org/10.3390/app12115506
Ali HEjaz WTaei MIqbal F(2019)Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON.2019.8936281(1092-1097)Online publication date: Oct-2019
https://doi.org/10.1109/IEMCON.2019.8936281
Raschip MCroitoru CFrasinaru C(2018)New Evolutionary Approaches for SAT Solving2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00086(522-526)Online publication date: Nov-2018
https://doi.org/10.1109/ICTAI.2018.00086
Mughal MShaikh Z(2018)River flow pattern recognition for flood mitigation2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/ICOMET.2018.8346312(1-6)Online publication date: Mar-2018
https://doi.org/10.1109/ICOMET.2018.8346312
Ali HLee D(2016)Solving the MAX-SAT problem by binary enhanced fireworks algorithm2016 Sixth International Conference on Innovative Computing Technology (INTECH)10.1109/INTECH.2016.7845071(204-209)Online publication date: Aug-2016
https://doi.org/10.1109/INTECH.2016.7845071
Ali HMitchell DLee D(2014)MAX-SAT problem using evolutionary algorithms2014 IEEE Symposium on Swarm Intelligence10.1109/SIS.2014.7011783(1-8)Online publication date: Dec-2014
https://doi.org/10.1109/SIS.2014.7011783

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