Book ChapterDOI
A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3-SAT Problems
Jeremy Frank
- pp 547-554
About:
The article was published on 1995-01-01. It has received 18 citations till now. The article focuses on the topics: Chromosome (genetic algorithm) & Genetic operator.read more
Citations
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Journal ArticleDOI
Evolutionary algorithms for the satisfiability problem
TL;DR: An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorithm for the satisfiability problem, indicating that evolutionary algorithms are competitive to WSAT.
Journal ArticleDOI
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
TL;DR: This paper describes 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 usingnVidia Compute Unified Device Architecture (CUDA).
Book ChapterDOI
Genetic Algorithm Behavior in the MAXSAT Domain
TL;DR: This work has shown that all nonzero Walsh coefficients can be computed exactly for MAXSAT problems in polynomial time using only the clause information, and unless P=NP, this low order information cannot reliably lead to the global optimum, thus nontrivial MAXSat problems must be deceptive.
Journal Article
Representations, fitness functions and genetic operators for the satisfiability problem
Jens Gottlieb,Nico Voss +1 more
TL;DR: In this paper, two genetic algorithms for SAT are presented, which mainly differ in the solution representation, i.e., the classical bit string representation and the path representation, with respect to their performance.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Proceedings Article
A new method for solving hard satisfiability problems
TL;DR: A greedy local search procedure called GSAT is introduced for solving propositional satisfiability problems and its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks.
Proceedings Article
Hard and easy distributions of SAT problems
TL;DR: It is shown that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult.