
Multi-Objective Evolutionary Algorithms
Data Structures, Convergence, and Diversity
Sanaz Mostaghim(Author)
Shaker (Publisher)
1st Edition
Published in January 2005
Book
Paperback/Softback
203 pages
978-3-8322-3661-8 (ISBN)
Description
Many real-world optimization problems consist of several conflicting objectives, the solutions of which is a set of trade-offs called the Pareto-optimal set. During the last decade, Evolutionary Algorithms (EAs) have been utilized to find an approximation of the Pareto-optimal set. However, the approximation set must possess solutions with high convergence towards the Pareto-optimal set and hold a good diversity in order to demonstrate a good approximation.
The subject of this thesis is to improve the existing Multi-Objective Evolutionary Algorithms (MOEAs) and to develop new techniques in order to achieve approximated sets with high convergence and diversity in low computational time.
The subject of this thesis is to improve the existing Multi-Objective Evolutionary Algorithms (MOEAs) and to develop new techniques in order to achieve approximated sets with high convergence and diversity in low computational time.
More details
Series
Thesis
Doctoral thesis
2004
Universität Paderborn
Edition
1., Aufl.
Language
English
Place of publication
Aachen
Germany
Target group
Professional and scholarly
Illustrations
74
74 s/w Abbildungen
74 illustrations
Dimensions
Height: 21 cm
Width: 14.8 cm
Weight
305 gr
ISBN-13
978-3-8322-3661-8 (9783832236618)
Schweitzer Classification