Evolutionary Computing for the Satisfiability Problem

Hao, Jin‐Kao; Lardeux, Frédéric; Saubion, Frédéric

doi:10.1007/3-540-36605-9_24

Cited by 15 publications

(4 citation statements)

References 16 publications

(17 reference statements)

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“…These include techniques such as Simulated Annealing [68,69], Evolutionary Algorithms [70], and Greedy Randomized Adaptive Search Procedures [71]. The nature-inspired GASAT algorithm [72] is a hybrid algorithm that combines a specific crossover and a tabu search procedure. The work in [73] proposes a hybrid approach called Iterated Robust Tabu Search (IRoTS) which combines an iterated local search and tabu search.…”

Section: Stochastic Local Search Algorithms (Sls)mentioning

confidence: 99%

A team of pursuit learning automata for solving deterministic optimization problems

2020

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Learning Automata (LA) is a popular decision-making mechanism to "determine the optimal action out of a set of allowable actions" [1]. The distinguishing characteristic of automata-based learning is that the search for an optimal parameter (or decision) is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself [2]. In this paper, we propose a novel LA paradigm that can solve a large class of deterministic optimization problems. Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic. In this paper, our proposed scheme can be seen as the counterpart of the family of pursuit LA developed for stochastic environments [3]. While classical pursuit LAs can pursue the action with the highest reward estimate, our pursuit LA rather pursues the collection of actions that yield the highest performance by invoking a team of LA. The theoretical analysis of the pursuit scheme does not follow classical LA proofs, and can pave the way towards more schemes where LA can be applied to solve deterministic optimization problems. Furthermore, we analyze the scheme under both a constant learning parameter and a time-decaying learning parameter. We provide some experimental results that show how our Pursuit-LA scheme can be used to solve the Maximum Satisfiability (Max-SAT) problem. To avoid premature convergence and better explore the search space, we enhance our scheme with the concept of artificial barriers recently introduced in [4]. Interestingly, although our scheme is simple by design, we observe that it performs well compared to sophisticated state-of-the-art approaches.

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Section: Stochastic Local Search Algorithms (Sls)mentioning

confidence: 99%

A team of pursuit learning automata for solving deterministic optimization problems

2020

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“…It relies on the management of a population of individuals which are submitted to recombination and local search operators. The earlier version of GASAT mentioned above (Hao et al, 2003) was developed with a simple local search process. In this section, the general scheme and the main components of the improved algorithm are defined.…”

Section: A Genetic Local Search Algorithm For Sat: Gasatmentioning

confidence: 99%

“…A first version of GASAT has been presented in (Hao et al, 2003). It uses a simple TS and the Corrective Clause crossover.…”

Section: Introductionmentioning

confidence: 99%

GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

Lardeux¹,

Saubion²,

Hao

2006

Evolutionary Computation

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This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

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“…Finally, the idea of a booklet dedicated to the benchmarks is appealing. Random: ALL on SAT funex (2) quantor (2) compsat (3) Jerusat1.3 (3) OepirC (3) CQuest (5) Forklift (5) OepirB (5) zchaff r and (5) OepirA (6) eqube1 (8) eqube2 (8) lsatv1.1 (8) nanosat (8) minilearning.jar (9) sato4.2 (10) wllsatv1 (10) brchaff (11) sato4.3 (11) zchaff (11) ISAT1 (12) Satzoo 1 .02 (13) ISAT2 (15) ISAT3 (19) march−eq−100 (26) Fig. 1.…”

Section: Is the Competition Relevant Or Not?mentioning

confidence: 99%

Fifty-Five Solvers in Vancouver: The SAT 2004 Competition

Berre

Simon

2005

Theory and Applications of Satisfiability Testing

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Abstract. For the third consecutive year, a SAT competition was organized as a joint event with the SAT conference. With 55 solvers from 25 author groups, the competition was a clear success. One of the noticeable facts from the 2004 competition is the superiority of incomplete solvers on satisfiable random k-SAT benchmarks. It can also be pointed out that the complete solvers awarded this year, namely Zchaff, jerusat1.3, satzoo-1.02, kncfsand march-eq, participated in the SAT 2003 competition (or at least former versions of those solvers). This paper is not reporting exhaustive competition results, already available in details online, but rather focuses on some remarkable results derived from the competition dataset.The SAT 2004 competition is ending a 3-year take-off period that attracted new SAT researchers and provided many new benchmarks and solvers to the community. The good participation rate of this year (despite the addition of the anti-blackbox rule) establishes the competition as an awaited yearly event. Some new directions for a new way of thinking about the competition are discussed at the end of the paper.

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Evolutionary Computing for the Satisfiability Problem

Cited by 15 publications

References 16 publications

A team of pursuit learning automata for solving deterministic optimization problems

A team of pursuit learning automata for solving deterministic optimization problems

GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

Fifty-Five Solvers in Vancouver: The SAT 2004 Competition

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