New Archive Based Evolutionary Multi-Objective Algorithms: Evolutionary Computation - Couverture souple

Esquivel, Xavier

Couverture souple

ISBN 10 : 3659184969 ISBN 13 : 9783659184963

Editeur : LAP LAMBERT Academic Publishing, 2012

A propos du livre

Présentation de l'éditeur

In this work we deal with the design of archive based multi-objective evolutionary algorithms (MOEAs) for the numerical treatment of multi objective optimization problems (MOPs). In particular, we design two generational operators­ one mutation and one crossover operator that are tailored to a class of archiving strategies and propose a new evolutionary strategy. Furthermore, we investigate here two widely used indicators for the evaluation of Multi-objective Evolutionary Algorithms, the Generational Distance (GD) and the Inverted Generational Distance (IGD), with respect to the properties of ametric. We define a new performance indicator, ∆p, which can be viewed as an ‘averaged Hausdorff distance’ between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators Generational Distance (GD) and Inverted Generational Distance (IGD). We will discuss theoretical properties of ∆p (as well as for GD and IGD) such as the metric properties and the compliance with state-of-the-art multi-objective evolutionary algorithms (MOEAs).

Biographie de l'auteur

Xavier has studied computer science at Instituto Politécnico Nacional in México city and he has obtained master degree in computer science at CINVESTAV-IPN. He currently works at Oracle as a software developer.

Détails de l'édition

• Éditeur: LAP LAMBERT Academic Publishing
• Date d'édition: 2012
• Langue: anglais
• ISBN 10: 3659184969
• ISBN 13: 9783659184963
• Reliure: Broché
• Nombre de pages: 124
• Coordonnées du fabricant: non disponible
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