Techniques to improve exploration efficiency of parallel self-adaptive genetic algorithms by dispensing with iteration and synchronization

  • Authors:

  • Eiichi Takashima;Yoshihiro Murata;Naoki Shibata;Minoru Ito

  • Affiliations:

  • Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan;Faculty of Economics, Shiga University, Hikone, 522-8522 Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan

  • Venue:
  • Systems and Computers in Japan
  • Year:
  • 2006

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Abstract

The exploration efficiency of GAs depends on parameter values such as the mutation rate and crossover rate. To save the labor of manually adjusting these values, GAs which automatically adjust parameters (adaptive GAs) have been proposed. However, most of the existing adaptive GAs can adjust only a few parameters simultaneously. Although several adaptive GAs can adjust many parameters simultaneously, these algorithms have a large computational cost.In this paper, we propose the Self-Adaptive Island GA (SAIGA) and its asynchronous implementation Asynchronous SAIGA (A-SAIGA). These two GAs are combinations of Meta GA and Island GA, and can adapt many parameters simultaneously with a computational cost equivalent to that of the simple GA. A-SAIGA improves exploration speed by avoiding synchronization between islands.Throughout our evaluation experiments, we confirmed that the performance of these GAs is close to that of the simple GA with optimal parameters. We also confirmed that A-SAIGA outperforms SAIGA in exploration speed. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(14): 25–33, 2006; Published online in Wiley InterScience (). DOI 10.1002/scj.20635