OEA_SAT: An Organizational Evolutionary Algorithm for solving SATisfiability problems

Jing, Liu; Jiang, Wenrong; Zhong, Wei; Jiao, Licheng

doi:10.1109/cec.2009.4983022

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…[A thorough review of previous applications of evolutionary algorithms to SAT can be found in Gottlieb et al (2002)] (Fukunaga, 2004). The state-of-the-art GAs for solving the SAT problems include GASAT (Lardeux et al, 2005;Hao et al, 2002;Lardeux et al, 2006), ELSA (Aksoy and Gunes, 2006), SAT-WAGA (Yingbiao, 2005), and OEA SAT (Liu et al, 2009).…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

“…SAT-WAGA can be obtained by e-mailing the author: isslyb@zsu.edu.cn. Liu et al (2009) introduce evolutionary algorithm for solving SAT called organisational evolutionary algorithm for SAT problems (OEA SAT). OEA SAT first divides the SAT problem into several sub-problems, and each organisation is composed of a sub problems (Liu et al, 2009).…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

“…Liu et al (2009) introduce evolutionary algorithm for solving SAT called organisational evolutionary algorithm for SAT problems (OEA SAT). OEA SAT first divides the SAT problem into several sub-problems, and each organisation is composed of a sub problems (Liu et al, 2009). The idea behind OEA SAT is to solve the sub-problem first, and then synthesise the solution for the original problem by adjusting the variables which have conflicts (Liu et al, 2009).…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

“…OEA SAT first divides the SAT problem into several sub-problems, and each organisation is composed of a sub problems (Liu et al, 2009). The idea behind OEA SAT is to solve the sub-problem first, and then synthesise the solution for the original problem by adjusting the variables which have conflicts (Liu et al, 2009). Li et al (2007b) integrate the evolutionary algorithms and the multiagent systems (MAEA-SAT) for solving SAT problems.…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

A survey of the satisfiability-problems solving algorithms

Kilani

Bsoul

Alsarhan

et al. 2013

IJAIP

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The propositional satisfiability (SAT) problem is one of the most fundamental problems in computer science. SAT solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. Empirical research has been very fruitful for the development of efficient methods for SAT problems, such as classical Davis-Putnam method, greedy SAT (GSAT) method and neural network SAT method. This paper gives a survey about the methods used for solving the SAT problems with an emphasis on surveying the local search algorithms.

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Section: The Genetic Algorithmsmentioning

confidence: 99%

Section: The Genetic Algorithmsmentioning

confidence: 99%

Section: The Genetic Algorithmsmentioning

confidence: 99%

Section: The Genetic Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

A survey of the satisfiability-problems solving algorithms

Kilani

Bsoul

Alsarhan

et al. 2013

IJAIP

View full text Add to dashboard Cite

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“…OEAs are a new kind of EA proposed in our previous works, and have been successfully applied to classification problems [6], numerical optimization problems [7], and satisfiability problems [8]. In this paper, three new operators, namely, the splitting operator, the annexing operator, and the training operator are designed on the basis of the MBS, so that the framework of the OEA is applicable to solving floorplanning problems.…”

Section: Introductionmentioning

confidence: 99%

Moving Block Sequence and Organizational Evolutionary Algorithm for General Floorplanning With Arbitrarily Shaped Rectilinear Blocks

Jing

Zhong

Jiao

et al. 2008

IEEE Trans. Evol. Computat.

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Abstract⎯A new nonslicing floorplan representation, the moving block sequence (MBS), is proposed in this paper. Our idea of the MBS originates from the observation that placing blocks on a chip has some similarities to playing the game, Tetris ® . Because no extra constraints are exerted on solution spaces, the MBS is not only useful for evolutionary algorithms, but also for dealing with rectangular, convex rectilinear, and concave rectilinear blocks, similarly and simultaneously, without partitioning rectilinear blocks into sub-blocks. This is owed to a special structure designed for recording the information of both convex and concave rectilinear blocks To test the performance of the MBS-OEA, benchmarks with hard rectangular, soft rectangular, and hard rectilinear blocks are used. The number of blocks in these benchmarks varies from 9 to 2 300. Also, the MBS-OEA and several well-designed existing algorithms are compared. The results show that the MBS-OEA can find high quality solutions for various problems.Additionally, the MBS-OEA shows a good performance in solving the problems with 300 hard rectangular blocks, 100 soft rectangular blocks, and 100 hybrid blocks, including both soft rectangular and hard rectilinear blocks. This illustrates that the MBS-OEA is not only suitable for solving a wide range of problems, but also competent for solving large-scale problems.Finally, a set of specific experiments is designed to identify the key component that is mainly responsible for the good performance of the MBS-OEA.

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Solving SAT problem with a multiagent evolutionary algorithm

Wang

Liu

et al. 2007

2007 IEEE Congress on Evolutionary Computation

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OEA_SAT: An Organizational Evolutionary Algorithm for solving SATisfiability problems

Cited by 3 publications

References 7 publications

A survey of the satisfiability-problems solving algorithms

A survey of the satisfiability-problems solving algorithms

Moving Block Sequence and Organizational Evolutionary Algorithm for General Floorplanning With Arbitrarily Shaped Rectilinear Blocks

Solving SAT problem with a multiagent evolutionary algorithm

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