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Accelerating the convergence of evolutionary algorithms by fitness landscape approximation

  • Alain Ratle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

A new algorithm is presented for accelerating the convergence of evolutionary optimization methods through a reduction in the number of fitness function calls. Such a reduction is obtained by 1) creating an approximate model of the fitness landscape using kriging interpolation, and 2) using this model instead of the original fitness function for evaluating some of the next generations. The main interest of the presented approach lies in problems for which the computational costs associated with fitness function evaluation is very high, such as in the case of most engineering design problems. Numerical results presented for a test case show that the reconstruction algorithm can effectively reduces the number of fitness function calls for simple problems as well as for difficult multidimensional ones.

Keywords

Fitness Function Covariance Function Fitness Landscape Nugget Effect Engineering Design Problem 
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 1998

Authors and Affiliations

  • Alain Ratle
    • 1
  1. 1.Département de génie mécaniqueUniversité de SherbrookeSherbrookeCanada

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