A Multi-Agent Organizational Framework for Coevolutionary Optimization

Danoy, Grégoire; Bouvry, Pascal; Boissier, Olivier

doi:10.1007/978-3-642-18222-8_9

Reference : A Multi-Agent Organizational Framework for Coevolutionary Optimization


	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/16742

Title :	A Multi-Agent Organizational Framework for Coevolutionary Optimization
Language :	English
Author, co-author :	Danoy, Grégoire [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Boissier, Olivier [ENSM.SE, G2I/SMA, Saint-Etienne, France]
Publication date :	2010
Journal title :	Lecture Notes in Computer Science
Publisher :	Springer
Volume :	4
Pages :	199-224
Peer reviewed :	Yes
ISSN :	0302-9743
e-ISSN :	1611-3349
Keywords :	[en] Multi-Agent Systems ; Organizational Model ; Evolutionary Algorithms
Abstract :	[en] This paper introduces DAFO, a Distributed Agent Framework for Optimization that helps in designing and applying Coevolutionary Genetic Algorithms (CGAs). CGAs have already proven to be efficient in solving hard optimization problems, however they have not been considered in the existing agent-based metaheuristics frameworks that currently provide limited organization models. As a solution, DAFO includes a complete organization and reorganization model, Multi-Agent System for EVolutionary Optimization (MAS4EVO), that permits to formalize CGAs structure, interactions and adaptation. Examples of existing and original CGAs modeled using MAS4EVO are provided and an experimental proof of their efficiency is given on an emergent topology control problem in mobile hybrid ad hoc networks called the injection network problem.
Permalink :	http://hdl.handle.net/10993/16742
DOI :	10.1007/978-3-642-18222-8_9
Other URL :	http://www.springerlink.com/content/t81676856q6j4n4x/

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