Description: Evolutionary Multi-objective Optimization in Uncertain Environments by Chi-Keong Goh, Kay Chen Tan "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Evolutionary algorithms are sophisticated search methods that have been found to 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 presence of 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. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties. Notes Presents recent results in Evolutionary Multi-objective Optimization in Uncertain Environments Back Cover Evolutionary algorithms are sophisticated search methods that have been found to 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 presence of 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. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties. Table of Contents I: Evolving Solution Sets in the Presence of Noise.- Noisy Evolutionary Multi-objective Optimization.- Handling Noise in Evolutionary Multi-objective Optimization.- Handling Noise in Evolutionary Neural Network Design.- II: Tracking Dynamic Multi-objective Landscapes.- Dynamic Evolutionary Multi-objective Optimization.- A Coevolutionary Paradigm for Dynamic Multi-Objective Optimization.- III: Evolving Robust Solution Sets.- Robust Evolutionary Multi-objective Optimization.- Evolving Robust Solutions in Multi-Objective Optimization.- Evolving Robust Routes.- Final Thoughts. Long Description Evolutionary algorithms are sophisticated search methods that have been found to 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 presence of 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. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties. Feature Presents recent results in Evolutionary Multi-objective Optimization in Uncertain Environments Details ISBN3540959750 Author Kay Chen Tan Short Title EVOLUTIONARY MULTI OBJECTIVE O Pages 271 Series Studies in Computational Intelligence Language English ISBN-10 3540959750 ISBN-13 9783540959755 Media Book Format Hardcover Series Number 186 Year 2009 Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K Place of Publication Berlin Country of Publication Germany Subtitle Issues and Algorithms Edition 2009th DOI 10.1007/978-3-540-95976-2 Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Edition Description 2009 ed. Publication Date 2009-03-09 Alternative 9783642101137 DEWEY 005.1 Audience Professional & Vocational Illustrations XI, 271 p. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 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ISBN-13: 9783540959755
Book Title: Evolutionary Multi-objective Optimization in Uncertain Environmen
Number of Pages: 271 Pages
Language: English
Publication Name: Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Publication Year: 2009
Subject: Engineering & Technology, Computer Science
Item Height: 235 mm
Item Weight: 1270 g
Type: Textbook
Author: Kay Chen Tan, Chi-Keong Goh
Item Width: 155 mm
Format: Hardcover
