ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Liefooghe, Arnaud; Jourdan, Laetitia; Legrand, Thomas; Humeau, Jérémie; Talbi, El-Ghazali

Document type :

Partie d'ouvrage

DOI :

10.1007/978-3-642-11218-8_5

Title :

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Author(s) :

Liefooghe, Arnaud [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]
Legrand, Thomas [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Humeau, Jérémie [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

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

Book title :

Advances in Multi-Objective Nature Inspired Computing

Publisher :

Springer Berlin / Heidelberg

Publication date :

2010

HAL domain(s) :

Informatique [cs]/Recherche opérationnelle [cs.RO]

English abstract : [en]

This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view ...
Show more >This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective 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 problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components.Show less >

Language :

Anglais

Audience :

Non spécifiée

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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