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

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

Document type :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-540-70928-2_31

Title :

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

Author(s) :

Liefooghe, Arnaud [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Basseur, Matthieu [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]

Conference title :

Evolutionary Multi-Criterion Optimization (EMO 2007)

City :

Matsushima

Country :

Japon

Start date of the conference :

2007-02

Journal title :

Lecture Notes in Computer Science (LNCS)

Publication date :

2007

HAL domain(s) :

Mathématiques [math]/Combinatoire [math.CO]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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https://hal.inria.fr/inria-00269972/document
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http://www.lifl.fr/~jourdan/publi/emoeomo.pdf
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https://hal.inria.fr/inria-00269972/document
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http://www.lifl.fr/~jourdan/publi/emoeomo.pdf
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