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Negative Space-Based Population Initialization Algorithm (NSPIA)

  • Krystian ŁapaEmail author
  • Krzysztof Cpałka
  • Andrzej Przybył
  • Konrad Grzanek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

There are many different varieties of population-based algorithms. They are interesting techniques for investigating of the search space of solutions and can be used, among others, to solve optimization problems. They usually start from initialization of a population of individuals, each of which encodes parameters of a single solution to the problem under consideration. After initialization, the preselected individuals are processed in a way that depends on the specifics of the algorithm. Therefore, properly implemented population initialization can significantly improve the algorithm’s operation and increase the quality of obtained results. This article describes a new population initialization algorithm. Its characteristic feature is the marginalization of those areas of the search space, in which once localized individuals were assessed as not satisfying. The proposed algorithm is of particular importance for problems in which no information is available that can improve the search procedure (black-box optimization). To test the proposed algorithm simulations were carried out using well-known benchmark functions.

Keywords

Population-based algorithms Population initialization Black-box optimization Negative space 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Krystian Łapa
    • 1
    Email author
  • Krzysztof Cpałka
    • 1
  • Andrzej Przybył
    • 1
  • Konrad Grzanek
    • 2
    • 3
  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesŁódźPoland
  3. 3.Clark UniversityWorcesterUSA

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