Introduction
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].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jaszkiewicz, A., Ishibuchi, H., Zhang, Q. (2012). Multiobjective Memetic Algorithms. In: Neri, F., Cotta, C., Moscato, P. (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-23247-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23246-6
Online ISBN: 978-3-642-23247-3
eBook Packages: EngineeringEngineering (R0)