Multiobjective Memetic Algorithms

Multiple conflicting points of view, which are often taken into account in real life applications, naturally result in a multiple objective optimization problem (MOP) [848]. In order to find the best compromise solution of a MOP, or a good approximation of it, Multiobjective Optimization (MOO) methods need some preference information from a decision maker. According to when and how the preference information is used in the solution procedure, MOO methods can be classified as either methods with a priori, a posteriori, or progressive (interactive) articulation of preferences [400].

Authors and Affiliations

1. Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland

   Andrzej Jaszkiewicz

2. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka, 599-8531, Japan

   Hisao Ishibuchi

3. The School of Computer Science & Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

   Qingfu Zhang

Editors and Affiliations

1. Dept. of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014, Jyväskylä, Finland

   Ferrante Neri

2. Escuela Técnica Superior de Ingeniería Informática, Universidad Málaga, Campus de Teatinos, s/n, 29071, Málaga, Spain

   Carlos Cotta

3. School of Electrical Engineering & Computer Science, University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia

   Pablo Moscato

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