A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3-SAT Problems

Frank, Jeremy

doi:10.1007/978-94-011-7108-3_57

Cited by 14 publications

(11 citation statements)

References 2 publications

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“…However, sGA suffers from problems like premature convergence and is seldom used to solve complex problems like MAX-SAT. Previous work in the area suggests the use of techniques like: larger population sizes, sub populations based pGAs [9] and niching. Moreover, a hybrid of GA and LS has also proved to be an order of magnitude faster than sGA [9,6].…”

Section: Related Work: Ga+ls To Solve Max-satmentioning

confidence: 99%

“…Previous work in the area suggests the use of techniques like: larger population sizes, sub populations based pGAs [9] and niching. Moreover, a hybrid of GA and LS has also proved to be an order of magnitude faster than sGA [9,6]. J. Frank [9] suggests the use of multiple sub-populations for a GA based MAX-SAT solver.…”

Section: Related Work: Ga+ls To Solve Max-satmentioning

confidence: 99%

“…Moreover, a hybrid of GA and LS has also proved to be an order of magnitude faster than sGA [9,6]. J. Frank [9] suggests the use of multiple sub-populations for a GA based MAX-SAT solver. This kind of island based pGAs can easily be implemented over conventional parallel architectures (like clusters).…”

Section: Related Work: Ga+ls To Solve Max-satmentioning

confidence: 99%

“…Approximate or Incomplete Algorithms: are mainly based on Local Search (LS) and Evolutionary Algorithms (EAs). Gsat [21], Tabu search [5,19], simulated annealing [11], Genetic Algorithms(GAs) [9] and scatter search [7] are all examples of incomplete algorithms for SAT. Hybrids of these algorithms are also in common use.…”

Section: Introductionmentioning

confidence: 99%

“…In their simple form, GAs have a tendency to lose their diversity prematurely, and hence their efficiency for solving MAX-SAT problem. In order to prevent this from happening different techniques are in common use, namely: increasing population size, using sub-populations based GAs [9], employing LS and niching. All these techniques come at an expense of increased computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

Munawar

Wahib

Munetomo

et al. 2009

Genet Program Evolvable Mach

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General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs) designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, same is the case with conventional LS algorithms. In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs. We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures. We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we obtain a speedup of 25x. We also discuss the effects of different optimization techniques on the overall execution time.

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Section: Related Work: Ga+ls To Solve Max-satmentioning

confidence: 99%