Evolutionary algorithms are sophisticated search methods that have been foundto be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presenceof uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. Presents recent results in Evolutionary Multi-objective Optimization in Uncertain Environments INDICE: Introduction.- A Distributed Cooperative Coevolutionary Algorithm for MO Optimization.- Hybrid Multi-objective Evolutionary Design for Neural Networks.- An Investigation on Noise-Induced Features in Robust Evolutionary Multi-Objective Optimization.- Conclusions.
- ISBN: 978-3-540-95975-5
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 350
- Fecha Publicación: 01/03/2009
- Nº Volúmenes: 1
- Idioma: Inglés