Evolving team compositions by agent swapping

Détails

Ressource 1Télécharger: BIB_A48B3D2E670D.P001.pdf (9688.38 [Ko])
Etat: Public
Version: Final published version
ID Serval
serval:BIB_A48B3D2E670D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Evolving team compositions by agent swapping
Périodique
IEEE Transactions on Evolutionary Computation
Auteur(s)
Lichocki P., Wischmann S., Keller L., Floreano D.
ISSN
1089-778X
Statut éditorial
Publié
Date de publication
2013
Peer-reviewed
Oui
Volume
17
Numéro
2
Pages
282-298
Langue
anglais
Résumé
Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have emerged as a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far, it has never been thoroughly quantified. Here, we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping (RAS) that exchanges only corresponding agents between teams and free agent swapping (FAS) that allows an arbitrary exchange of agents. Our results show that RAS suffers from premature convergence, whereas FAS entails insufficient convergence. Consequently, in both cases, the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem, we propose combining the two methods. Our approach first applies FAS to explore the search space and then RAS to exploit it. This mixed approach is a much more efficient strategy for the evolution of team compositions compared to either strategy on its own. Our results suggest that such a mixed agent-swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.
Mots-clé
Cooperation, crossover, evolutionary computation, multiagent systems, team composition, team optimization
Web of science
Création de la notice
05/03/2012 11:36
Dernière modification de la notice
20/08/2019 15:09
Données d'usage