Abraham / Goldberg Evolutionary Multiobjective Optimization

Theoretical Advances and Applications
1. Auflage 2005
ISBN: 978-1-84628-137-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 302 Seiten, eBook

Reihe: Advanced Information and Knowledge Processing

Evolutionary Multiobjective Optimization is a rare collection of the latest state-of-the-art theoretical research, design challenges and applications in the field of multiobjective optimization paradigms using evolutionary algorithms. It includes two introductory chapters giving all the fundamental definitions, several complex test functions and a practical problem involving the multiobjective optimization of space structures under static and seismic loading conditions used to illustrate the various multiobjective optimization concepts. Important features include:Detailed overview of all the multiobjective optimization paradigms using evolutionary algorithmsExcellent coverage of timely, advanced multiobjective optimization topicsState-of-the-art theoretical research and application developmentsChapters authored by pioneers in the field Academics and industrial scientists as well as engineers engaged in research, development and application of evolutionary algorithm based Multiobjective Optimization will find the comprehensive coverage of this book invaluable.

Zielgruppe

Research

Autoren/Hrsg.

Abraham, Ajith

Goldberg, Robert

Inhaltsverzeichnis

Evolutionary Multiobjective Optimization.- Recent Trends in Evolutionary Multiobjective Optimization.- Self-adaptation and Convergence of Multiobjective Evolutionary Algorithms in Continuous Search Spaces.- A Simple Approach to Evolutionary Multiobjective Optimization.- Quad-trees: A Data Structure for Storing Pareto Sets in Multiobjective Evolutionary Algorithms with Elitism.- Scalable Test Problems for Evolutionary Multiobjective Optimization.- Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems.- Evolving Continuous Pareto Regions.- MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme.- Use of Multiobjective Optimization Concepts to Handle Constraints in Genetic Algorithms.- Multi-criteria Optimization of Finite State Automata: Maximizing Performance while Minimizing Description Length.- Multiobjective Optimization of Space Structures under Static and Seismic Loading Conditions.