Efficient Optimization by Differential Evolution using Rough Approximation Model with Adaptive Control of Error Margin

• Takahama Tetsuyuki

  Hiroshima City University

• Sakai Setsuko

  Hiroshima Shudo University

Abstract

In this study, we propose to utilize a rough approximation model, which is an approximation model with low accuracy and without learning process, in order to reduce the number of function evaluations effectively. Although the approximation errors between the true function values and the approximation values are not small, the rough model can estimate the order relation of solutions with fair accuracy. In order to use this nature of the rough model, we have proposed estimated comparison method, in which function evaluations are omitted when the order relation of solutions can be judged by approximation values. In the method, a parameter for error margin is introduced to avoid incorrect judgment. In order to improve the efficiency of the method, we propose adaptive control of the margin parameter based on success rate of the judgment. Also, we propose to avoid omitting promising solutions by utilizing congestion of solutions. The advantage of these improvements is shown by comparing the results obtained by Differential Evolution (DE), original DE with estimated comparison method, and improved method in various types of benchmark functions.

Journal

• SCIS & ISIS

  SCIS & ISIS 2008 (0), 1238-1238, 2008

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Evolutionary Computation
• Differential Evolution
• Approximation Model

Details

• CRID: 1390001205589490944
• NII Article ID: 130004672725
• DOI: 10.14864/softscis.2008.0.1238.0
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed