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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2007: Evolutionary Multi-Criterion Optimization pp 386–400Cite as

ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4403)

Abstract

This paper presents ParadisEO-MOEO, a white-box object-oriented generic framework dedicated to the flexible design of evolutionary multi-objective algorithms. This paradigm-free software embeds some features and techniques for Pareto-based resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the multi-objective problems they are intended to solve. This separation confers a maximum design and code reuse. ParadisEO-MOEO provides a broad range of archive-related features (such as elitism or performance metrics) and the most common Pareto-based fitness assignment strategies (MOGA, NSGA, SPEA, IBEA and more). Furthermore, parallel and distributed models as well as hybridization mechanisms can be applied to an algorithm designed within ParadisEO-MOEO using the whole version of ParadisEO. In addition, GUIMOO, a platform-independant free software dedicated to results analysis for multi-objective problems, is briefly introduced.

Keywords

  • object-oriented frameworks
  • design and code reuse
  • multi-objective optimization
  • evolutionary algorithms

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Authors and Affiliations

  1. INRIA Futurs, Laboratoire d’Informatique Fondamentale de Lille (LIFL), CNRS, Bât. M3, Cité Scientifique, 59655 Villeneuve d’Ascq cedex, France

    Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan & El-Ghazali Talbi

Authors
  1. Arnaud Liefooghe
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  2. Matthieu Basseur
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  3. Laetitia Jourdan
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  4. El-Ghazali Talbi
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Editor information

Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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© 2007 Springer Berlin Heidelberg

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Liefooghe, A., Basseur, M., Jourdan, L., Talbi, EG. (2007). ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_31

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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