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

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.
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Contributor : Arnaud Liefooghe <>
Submitted on : Thursday, April 3, 2008 - 1:42:08 PM
Last modification on : Thursday, May 28, 2020 - 9:22:09 AM
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Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan, El-Ghazali Talbi. ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization. Evolutionary Multi-Criterion Optimization (EMO 2007), Feb 2007, Matsushima, Japan. pp.386-400, ⟨10.1007/978-3-540-70928-2_31⟩. ⟨inria-00269972⟩



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