A Multilevel Memetic Algorithm for Large SAT-Encoded Problems

Bouhmala, Noureddine

doi:10.1162/evco_a_00078

Cited by 19 publications

(8 citation statements)

References 30 publications

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“…Looking at the time spent MLVL-K-Means, in all the cases requires the least amount of time (up to 99% faster).With regard to the multilevel paradigm, it is somewhat unsatisfactory that its ability to enhance the convergence behavior of the two algorithms is not conclusive. However, This does not seem to be in line with with the general success established in other combinatorial optimization problems such as the graph partitioning problem [16] and the satisfiability problem [17]. The reason behind this sort of convergence behaviour observed in the multilevel paradigm is not obvious but we can speculate.…”

Section: Analysis Of Resultsmentioning

confidence: 57%

Multilevel Techniques for the Clustering Problem

Bouhmala¹

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Analysis Of Resultsmentioning

confidence: 57%

Multilevel Techniques for the Clustering Problem

Bouhmala¹

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…As future work, we aim to investigate further improving the performance of the pursuit scheme in terms of convergence speed and exploration of the search space using Multilevel techniques introduced by Bouhmala [57,58].…”

Section: Resultsmentioning

confidence: 99%

“…The work conducted in [56] proposes an adaptive memory based local search algorithm that exploits various strategies in order to guide the search to achieve a suitable trade-off between intensification and diversification. Multilevel techniques [57,58] have been applied to Max-SAT with considerable success. They progressively coarsen the problem, find an assignment, and then employ a metaheuristic to refine the assignment on each of the coarsened problems in reverse order.…”

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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“…Mann-Whitney U-test gave identical results. The non-parametric effect size measure b A 12 [21] was used to evaluate the relative dominance of one algorithm over the other. The b A 12 effect size measure is calculated using the rank sum which is a common component in any non-parametric analysis such as the Mann-Whitney U-test [20].…”

Section: Discussionmentioning

confidence: 99%

A Multilevel Evolutionary Algorithm Applied to the Maximum Satisfiability Problems

Bouhmala¹,

Øvergård²,

Hjelmervik³

2018

Machine Learning - Advanced Techniques and Emerging Applications

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The maximum satisfiability problem that is known to be nondeterministic polynomial (NP) complete plays a central role problem in many applications in the fields of very large-scale integration (VLSI) computer-aided design, computing theory, artificial intelligence, and defense. Given a set of m clauses and n Boolean variables, the maximum satisfiability problem refers to the task of finding an assignment of values to the variables that maximizes the number of satisfied clauses (or minimizes the number of unsatisfied clauses) In this chapter, a multilevel evolutionary algorithm is proposed for the maximum satisfiability problem. The multilevel process works by grouping the variables defining the problem to form clusters, uses the clusters to define a new problem, and is repeated until the problem size falls below some threshold. The coarsest problem is then given an initial assignment of values to variables and the assignment is successively refined on all the problems starting with the coarsest and ending with the original.

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A Multilevel Memetic Algorithm for Large SAT-Encoded Problems

Cited by 19 publications

References 30 publications

Multilevel Techniques for the Clustering Problem

Multilevel Techniques for the Clustering Problem

A team of pursuit learning automata for solving deterministic optimization problems

A Multilevel Evolutionary Algorithm Applied to the Maximum Satisfiability Problems

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