A genetic algorithm with expansion and exploration operators for the maximum satisfiability problem

Gorbenko, Anna; Popov, Vladimir

doi:10.12988/ams.2013.13107

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Sat Solvers For Sp and Spp1

Previous Ga Approaches1

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2013

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Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Note that some SAT solvers for SP and SPP were considered in [16,18]. In this section, we consider A1 genetic algorithm with expansion and exploration operators for MAXSAT (see [22]);…”

Section: Sat Solvers For Sp and Sppmentioning

confidence: 99%

Problems of sensor placement for intelligent environments of robotic testbeds

Sheka¹

2013

ijma

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In this paper, we consider problems of sensor placement for intelligent environments of robotic testbeds. In particular, we consider the problem of sensor placement and the problem of sensor placement for triangulation based localization. We present experimental results for these problems. Also, we use artificial physics optimization algorithms for solution of the problem of sensor placement. We consider artificial physics optimization for different virtual forces. In particular, we use Runge Kutta neural networks for the calculation of values of virtual forces.

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“…Note that some SAT solvers for SP and SPP were considered in [16,18]. In this section, we consider A1 genetic algorithm with expansion and exploration operators for MAXSAT (see [22]);…”

Section: Sat Solvers For Sp and Sppmentioning

confidence: 99%

Problems of sensor placement for intelligent environments of robotic testbeds

Sheka¹

2013

ijma

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“…Like all GAs, this method is prone to getting stuck at local optima. Several methods were attempted in aiding the algorithm to escape local optima such as the weighted maxSAT fitness function [6,2], the use of a high mutation rate combined with hill climbing [8], and the use of more sophisticated recombination operators [7,5]. Out of all these methods, GASAT is, to the authors' knowledge, the most promising and best implementation of using GA for solving the SAT problem.…”

Section: Previous Ga Approachesmentioning

confidence: 99%

pEvoSAT

Shabash

Wiese

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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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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A genetic algorithm with expansion and exploration operators for the maximum satisfiability problem

Cited by 2 publications

References 16 publications

Problems of sensor placement for intelligent environments of robotic testbeds

Problems of sensor placement for intelligent environments of robotic testbeds

pEvoSAT

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