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Multiobjective Evolutionary Strategy for Finding Neighbourhoods of Pareto-optimal Solutions

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Applications of Evolutionary Computation (EvoApplications 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7835))

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Abstract

In some cases of Multiobjective Optimization problems finding Pareto optimal solutions does not give enough knowledge about the shape of the landscape, especially with multimodal problems and non-connected Pareto fronts. In this paper we present a strategy which combines a hierarchic genetic algorithm consisting of multiple populations with rank selection. This strategy aims at finding neighbourhoods of solutions by recognizing regions with high density of individuals. We compare two variants of the presented strategy on a benchmark two-criteria minimization problem.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Ewa Gajda-Zagórska

Authors
  1. Ewa Gajda-Zagórska
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Editors and Affiliations

  1. S2 Grupo, C/ Ramiro de Maeztu 7, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

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Gajda-Zagórska, E. (2013). Multiobjective Evolutionary Strategy for Finding Neighbourhoods of Pareto-optimal Solutions. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-37192-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37191-2

  • Online ISBN: 978-3-642-37192-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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