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Authors: Lamia Sadeg-Belkacem 1 ; Zineb Habbas 2 and Wassila Aggoune-Mtalaa 3

Affiliations: 1 Ecole Nationale Superieure d’Informatique,, University of Lorraine and Military Polytechnic School, Algeria ; 2 University of Lorraine, France ; 3 Public Research Centre Henri Tudor, Luxembourg

ISBN: 978-989-758-074-1

Keyword(s): Optimization Problems, Partial Constraint Satisfaction Problems, Graph Decomposition, Adaptive Genetic Algorithm (AGA), AGA Guided by Decomposition.

Related Ontology Subjects/Areas/Topics: AI and Creativity ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Constraint Satisfaction ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing ; Symbolic Systems

Abstract: Solving a Partial Constraint Satisfaction Problem consists in assigning values to all the variables of the problem such that a maximal subset of the constraints is satisfied. An efficient algorithm for large instances of such problems which are NP-hard does not exist yet. Decomposition methods enable to detect and exploit some crucial structures of the problems like the clusters, or the cuts, and then apply that knowledge to solve the problem. This knowledge can be explored by solving the different sub-problems separately before combining all the partial solutions in order to obtain a global one. This was the focus of a previous work which led to some generic algorithms based on decomposition and using an adaptive genetic algorithm, for solving the subproblems induced by the crucial structures coming from the decomposition. This paper aims to explore the decomposition differently. Indeed, here the knowledge is used to improve this adaptive genetic algorithm. A new adaptive genetic al gorithm guided by structural knowledge is proposed. It is designed to be generic in order that any decomposition method can be used and different heuristics for the genetic operators are possible. To prove the effectiveness of this approach, three heuristics for the crossover step are investigated. (More)

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Paper citation in several formats:
Sadeg-Belkacem, L.; Habbas, Z. and Aggoune-Mtalaa, W. (2015). AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-074-1, pages 78-89. DOI: 10.5220/0005196400780089

@conference{icaart15,
author={Lamia Sadeg{-}Belkacem. and Zineb Habbas. and Wassila Aggoune{-}Mtalaa.},
title={AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2015},
pages={78-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005196400780089},
isbn={978-989-758-074-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs
SN - 978-989-758-074-1
AU - Sadeg-Belkacem, L.
AU - Habbas, Z.
AU - Aggoune-Mtalaa, W.
PY - 2015
SP - 78
EP - 89
DO - 10.5220/0005196400780089

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