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Kinship-based differential evolution algorithm for unconstrained numerical optimization

  • Giovanni Formica
  • Franco MilicchioEmail author
Original paper
  • 32 Downloads

Abstract

We propose a modification of the standard differential evolution (DE) algorithm in order to significantly make easier and more efficient standard DE implementations. Taking advantages from chaotic map approaches, recently proposed and successfully implemented for swarm intelligence-based algorithms, our DE improvement facilitates the search for the best population and then the optimal solution. More specifically, we work with a genetic memory that stores parents and grandparents of each individual (its kin) of the population generated by the DE algorithm. In this way, the new population is carried out not only on the basis of the best fitness of a certain individual, but also according to a good score of its kin. Additionally, we carried out a wide numerical campaign in order to assess the performances of our approach and validated the results with standard statistical techniques.

Keywords

Optimization Differential evolution algorithm Chaotic maps 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Dipartimento di ArchitetturaRoma Tre UniversityRomeItaly
  2. 2.Dipartimento di IngegneriaRoma Tre UniversityRomeItaly

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