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Effects of Adding Perturbations to Phenotypic Parameters in Genetic Algorithms for Searching Robust Solutions

  • Shigeyoshi Tsutsui
  • Ashish Ghosh
Part of the Natural Computing Series book series (NCS)

Abstract

We have proposed a scheme that extends the application of GAs to domains that require detection of robust solutions. We called this technique GAs/RS3 — GAs with a robust solution searching scheme. In the GAs/RS3, a perturbation is added to the phenotypic feature once for evaluation of an individual, thereby reducing the chance of selecting sharp peaks. We refer to this method as a single-evaluation model (SEM). In this chapter, we introduce a natural variant of this method, a multi-evaluation-model (MEM), where perturbations are given more than once for evaluation of the individual, and we offer comparative studies on their convergence property. The results showed that for the GAs/RS with SEM the population converges to robust solutions faster than with the MEM, and as the number of evaluations increases, the convergence speed decreases. We may conclude that the GAs/RS3 with the SEM is more efficient than with the MEM. We also introduced a variation of the MEM, i.e., multievaluation model keeping the worst value (MEM-W), and provided a mathematical analysis.

Keywords

Genetic Algorithm Reduction Factor Robust Solution Convergence Process Phenotypic Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Shigeyoshi Tsutsui
    • 1
  • Ashish Ghosh
    • 2
  1. 1.Department of Management and Information ScienceHannan UniversityMatsubaraJapan
  2. 2.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

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