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Meta-Optimization of Mind Evolutionary Computation Algorithm Using Design of Experiments

  • Maxim Sakharov
  • Anatoly Karpenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

This paper presents a new technique for solving a meta-optimization problem for Mind Evolutionary Computation (MEC) algorithm using a full factorial designed experiment. This approach can be also generalized for other global optimization population-based algorithms. In general, design of experiments allows one to determine the influence of input factors and their interaction on the output of a process. It’s proposed to use such an approach to identify the most important free parameters as well as to estimate their interaction and determine the optimal values of those parameters for specific classes of objective functions. The paper contains the description of proposed method and software implementation along with the results of numerical experiments conducted to determine optimal values of the MEC algorithm’s free parameters.

Keywords

Mind Evolutionary Computation Global optimization Meta-optimization Design of experiments 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bauman MSTUMoscowRussia

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