NDLI: E.: ParadisEO-MOEO: A framework for evolutionary multi-objective optimization. In: Evolutionary multicriterion optimization (2007)

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E.: ParadisEO-MOEO: A framework for evolutionary multi-objective optimization. In: Evolutionary multicriterion optimization (2007)

Content Provider	CiteSeerX
Author	Jourdan, Laetitia Basseur, Matthieu Liefooghe, Arnaud Talbi, El-Ghazali
Abstract	Abstract. This paper presents ParadisEO-MOEO, a white-box object-oriented generic framework dedicated to the flexible design of evolu-tionary 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 concep-tual distinction between the solution methods and the multi-objective problems they are intended to solve. This separation confers a maxi-mum 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 al-gorithm 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 in-troduced. Key words: object-oriented frameworks, design and code reuse, multi-objective optimization, evolutionary algorithms 1
File Format	PDF
Publisher Date	2007-01-01
Access Restriction	Open
Subject Keyword	Archive-related Feature Evolutionary Multi-objective Optimization Maxi-mum Design White-box Object-oriented Generic Framework Whole Version Distributed Model Hybridization Mechanism Pareto-based Resolution Efficient Program Solution Method Flexible Design Result Analysis Platform-independant Free Software Multi-objective Optimization Object-oriented Framework Clear Concep-tual Distinction Evolu-tionary Multi-objective Algorithm Multi-objective Problem Paradigm-free Software Common Pareto-based Fitness Assignment Strategy Evolutionary Algorithm Performance Metric Broad Range Code Reuse Evolutionary Multicriterion Optimization
Content Type	Text
Resource Type	Article

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