GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

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

doi:10.1162/evco.2006.14.2.223

Cited by 67 publications

(52 citation statements)

References 23 publications

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“…First, the compass approach addresses the evaluation of both the fitness and the diversity while DBR only considers the diversity, being focused on the evaluation of crossover operators exclusively. Secondly, compass makes use of the Hamming distance entropy as in Lardeux et al (2006) to measure the population diversity, while DBR deals with both the average pairwise distance of the offspring as well as the distance from the parent population using the CARP based diversity measure shown in Fig. 1. …”

Section: Diversity-based Reward (Dbr)mentioning

confidence: 99%

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

et al. 2016

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Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results.

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Section: Diversity-based Reward (Dbr)mentioning

confidence: 99%

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

et al. 2016

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“…This kind of hybridization is called memetic algorithms [31]. Recently, several memetic algorithms were proposed to deal with the Max Sat problems for example: FlipGA [9] based on hybrid genetic algorithm and a special flip heuristics; GASAT [8] based on hybrid genetic algorithm and tabu search and QGASAT [32] based on quantum evolutionary algorithm and an adapted quantum local search procedure. Finally, a new kind of hybridization based on incomplete and complete algorithms has emerged.…”

Section: State-of-the-art Sat Solving Algorithmsmentioning

confidence: 99%

“…For example the figure 6 shows an example of quantum crossover. It should be noted that is better to use specific crossover operations more adapted for the max sat problems like those used in GASAT [8]. View that the quantum representation offers a great diversity; it is preferably to use small values for the probabilities of mutation and crossover in order to keep good performance of GQA.…”

Section: Crossover Operatorsmentioning

confidence: 99%

“…Therefore, they do not guarantee optimality. This class of methods encompasses Evolutionary Algorithms (EA) [8,9], Stochastic Local Search (SLS) methods [10,11] and hybrid methods [12,13] One of the iterative methods that have been used recently to solve this type of problem is Genetic Algorithms (GA) [14,15]. It is a stochastic iterative algorithm that operates on a population of individuals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Quantum Genetic Algorithm and Local Search based DPLL for Max 3-SAT Problems

Layeb¹,

Saïdouni²

2014

Appl. Math. Inf. Sci.

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Abstract:In this paper, we present a new framework for combining complete and incomplete methods in order to deal with the Max Sat problem. The objective is to find the best assignment for a set of Boolean variables, which gives the maximum of verified clauses in a Boolean formula. Unfortunately, this problem has been shown to be NP-hard (non-deterministic polynomial-time hard) if the number of variables per clause is greater than 3. The proposed approach is based on quantum inspired genetic principles and the well known exact algorithm DPLL. The underlying idea is to harness the optimization capabilities of quantum genetic algorithm to achieve good quality solutions for Max SAT problem. A new local search based on DPLL algorithm has been embodied in the quantum genetic algorithm leading to an efficient hybrid framework which achieves better balance between diversification and intensification search. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed hybrid approach.

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“…Finally, Lardeux at al. [14] propose the GASAT algorithm, based on evolutionary algorithms and tabu search for SAT. The GASAT algorithm consists of a recombination stage based on a specific crossover and a tabu search stage.…”

Section: Related Workmentioning

confidence: 99%

Improvements to Hybrid Incremental SAT Algorithms

Letombe

Marques-Silva

Theory and Applications of Satisfiability Testing – SAT 2008

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Abstract. Boolean Satisfiability (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. SAT solvers are also the building block of more sophisticated decision procedures, including Satisfiability Modulo Theory (SMT) solvers. The large number of applications of SAT yields ever more challenging problem instances, and motivate the development of more efficient algorithms. Recent work studied hybrid approaches for SAT, which involves integrating incomplete and complete SAT solvers. This paper proposes a number of improvements to hybrid SAT solvers. Experimental results demonstrate that the proposed optimizations are effective. The resulting algorithms in general perform better and, more importantly, are significantly more robust.

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GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

Cited by 67 publications

References 23 publications

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

A Hybrid Quantum Genetic Algorithm and Local Search based DPLL for Max 3-SAT Problems

Improvements to Hybrid Incremental SAT Algorithms

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