Application of the parameter-free genetic algorithm to the fixed channel assignment problem

  • Authors:

  • Shouichi Matsui;Isamu Watanabe;Ken-Ichi Tokoro

  • Affiliations:

  • Communication & Information Research Laboratory, Central Research Institute of the Electric Power Industry, Komae, 201-8511 Japan;Communication & Information Research Laboratory, Central Research Institute of the Electric Power Industry, Komae, 201-8511 Japan;Communication & Information Research Laboratory, Central Research Institute of the Electric Power Industry, Komae, 201-8511 Japan

  • Venue:
  • Systems and Computers in Japan
  • Year:
  • 2005

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Abstract

This paper is concerned with the application of the parameter-free genetic algorithm (PfGA) proposed by Sawai and colleagues and the parallel distributed PfGA, to the fixed channel assignment problem. The results of the investigation are presented. The PfGA does not include parameters such as the population size, the crossover rate, and the mutation rate, which have been indispensable in the conventional genetic algorithm (GA). This eliminates parameter tuning for each problem, which is a very useful property in practice. Although the applications of PfGA to function optimization in 5 to 10 dimensions have been reported, there has been no report on the performance when it is applied to combinatorial optimization with a larger search space, and it remains a practically important problem to examine the performance in such a case. This paper considers the application of the PfGA to the fixed channel assignment problem, which is a combinatorial optimization problem. The sequence representation by the random keys is investigated, and it is shown that the PfGA and the parallel distributed PfGA have as high a performance in the fixed channel assignment problem as in the function optimization problem. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(4): 71–81, 2005; Published online in Wiley InterScience (). DOI 10.1002/scj.10328