Efficient Genetic Algorithms Using Simple Genes Exchange LocalSearch Policy for the Quadratic Assignment Problem

  • Authors:

  • M. H. Lim;Y. Yuan;S. Omatu

  • Affiliations:

  • School of Electrical and Electronic Engineering, Block S1, Nanyang Technological University, Nanyang Avenue, Singapore 639798. emhlim@ntu.edu.sg;School of Electrical and Electronic Engineering, Block S1, Nanyang Technological University, Nanyang Avenue, Singapore 639798;Department of Computer and Systems Sciences, College of Engineering, Osaka Prefecture University, Sakai, Osaka 593, Japan. omatu@cs.osakafu-u.ac.jp

  • Venue:
  • Computational Optimization and Applications
  • Year:
  • 2000

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Abstract

In this paper, we describe an approach for solving the quadraticassignment problem (QAP) that is based on genetic algorithms (GA). Itwill be shown that a standard canonical GA (SGA), which involvesgenetic operators of selection, reproduction, crossover, andmutation, tends to fall short of the desired performance expected ofa search algorithm. The performance deteriorates significantly as thesize of the problem increases. To address this syndrome, it is commonfor GA-based techniques to be embedded with deterministic localsearch procedures. It is proposed that the local search shouldinvolve simple procedure of genome reordering that should not be toocomplex. More importantly, from a computational point of view, thelocal search should not carry with it the full cost of evaluating achromosome after each move in the localized landscape. Results ofsimulation on several difficult QAP benchmarks showed theeffectiveness of our approaches.