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Combining Genetic Algorithm with the Multilevel Paradigm for the Maximum Constraint Satisfaction Problem

  • Noureddine BouhmalaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This paper introduces a hybrid approach combining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promising performances achieved by the proposed approach are demonstrated by comparisons made to solve conventional random benchmark problems.

Keywords

Maximum constraint satisfaction problem Genetic algorithms Multilevel paradigm 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Maritime Technology and InnovationUniversity of SouthEastHortenNorway

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