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Multi-memetic Mind Evolutionary Computation Algorithm Based on the Landscape Analysis

  • Maxim Sakharov
  • Anatoly Karpenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

This paper presents a new multi-memetic modification of the Mind Evolutionary Computation (MEC) algorithm with the incorporated landscape analysis (LA) for solving global optimization problems. The proposed landscape analysis is based on the concept of Lebesgue integral and allows one to divide objective functions into three categories. Each category suggests a usage of specific hyper-heuristics for adaptive meme selection. Software implementation of the proposed method is briefly described in the paper. Efficiency of the method was compared with the multi-memetic modification of the MEC algorithm which utilizes a simple random hyper-heuristic only, without any LA procedure. Comparative performance investigation was carried out with a use of high-dimensional benchmark functions of various classes.

Keywords

Multi-memetic algorithm Landscape analysis Mind evolutionary computation Global optimization 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Bauman MSTUMoscowRussia

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