Artificial Darwinism: an Overview. Darwinisme Artificiel: une Vue d’Ensemble

Artificial Darwinism: an Overview

Darwinisme Artificiel: une Vue d’Ensemble

Evelyne Lutton

INRIA, Rocquencourt, Équipe COMPLEX, Projet FRACTALES, B.P. 105, 78153 Le Chesnay Cedex, France

Page: 
339-354
|
Received: 
10 February 2004
|
Accepted: 
N/A
|
Published: 
30 September 2005
| Citation

OPEN ACCESS

Abstract: 

Genetic algorithms,genetic programming,evolution strategies,and what is now called evolutionary algorithms,are stochastic optimisation techniques inspired by Darwin’s theory.We present here an overview of these techniques,while stressing on the extreme versatility of the artificial evolution concept.Their applicative framework is very large and is not limited to pure optimisation.Artifical evolution implementations are however computationally expensive:an efficient tuning of the components and parameter of these algorithms should be based on a clear comprehension of the evolutionary mechanisms.Moreover,it is noticeable that the killer-applications of the domain are for the most part based on hybridisation with other optimisation techniques.As a consequence,evolutionary algorithms are not to be considered in competition but rather in complement to the “classical”optimisation techniques.

Résumé

Les algorithmes génétiques,la programmation génétique,les stratégies d’évolution,et ce que l’on appelle maintenant en général les algorithmes évolutionnaires,sont des techniques d’optimisation stochastiques inspirées de la théorie de l’évolution selon Darwin. Nous donnons ici une vision globale de ces techniques, en insistant sur l’extrême flexibilité du concept d’évolution artificielle. Cet outil a un champ très vaste d’applications,qui ne se limite pas à l’optimisation pure. Leur mise en œuvre se fait cependant au prix d’un coût calculatoire important,d’où la nécessité de bien comprendre ces mécanismes d’évolution pour adapter et régler efficacement les différentes composantes de ces algorithmes. Par ailleurs,on note que les applications-phares de ce domaine sont assez souvent fondées sur une hybridation avec d’autres techniques d’optimisation. Les algorithmes évolutionnaires ne sont donc pas à considérer comme une méthode d’optimisation concurrente des méthodes d’optimisation classiques,mais plutôt comme une approche complémentaire.

Keywords: 

Genetic Algorithms,genetic programing,evolution stategies,evolutionary algorithms,artificial evolution,artificial Darwinism,stochastic optimisation.

Mots clés 

Algorithmes génétiques,programmation génétique,stratégies d’évolution,algorithmes évolutionnaires,évolution artificielle,Darwinisme artificiel,optimisation stochastique.

1. L’Évolution Artificielle, la Théorie de Darwin en Informatique
2. Les Outils Évolutionnaires de Base
3. Aspects Théoriques
4. Au-Delà de l’Optimisation
5. Conclusion et Perspectives
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