A cooperative tree-based hybrid GA-B&B approach for solving challenging permutation-based problems.

Authors:
Malika Mehdi;Jean-Claude Charr;Nouredine Melab;El-Ghazali Talbi;Pascal Bouvry
Affiliations:
University of Luxembourg, Luxembourg, Luxembourg;INRIA Lille Nord-Europe, Lille, France;INRIA Lille Nord-Europe, Lille, France;INRIA Lille Nord-Europe, Lille, France;University of Luxembourg, Luxembourg, Luxembourg
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

The issue addressed in this paper is how to build low-level hybrid cooperative optimization methods that combine a Genetic Algorithm (GA) with a Branch-and-Bound algorithm (B&B). The key challenge is to provide a common solution and search space coding and associated transformation operators enabling an efficient cooperation between the two algorithms. The tree-based coding is traditionally used in exact optimization methods such as B&B. In this paper, we explore the idea of using such coding in Genetic Algorithms. Following this idea, we propose a pioneering approach hybridizing a GA with a B&B algorithm. The information (solutions and search sub-spaces) exchange between the two algorithms is performed at low-level and during the exploration process. From the implementation point of view, the common coding has facilitated the low-level coupling of two software frameworks: ParadisEO and BOB++ used to implement respectively the GA and the B&B algorithms. The proposed approach has been experimented on the 3D Quadratic Assignment Problem. In order to support the CPU cost of the hybridization mechanism, hierarchical parallel computing is used together with grid computing.