Computational Intelligence for Optimization

Nirwan Ansari

ISBN 10: 1461379075 / ISBN 13: 9781461379072

Published by Springer, Springer, 2012

About this Item

Druck auf Anfrage Neuware - Printed after ordering - The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.

Seller Inventory # 9781461379072

Bibliographic Details

• Title: Computational Intelligence for Optimization
• Publisher: Springer, Springer
• Publication Date: 2012
• Language: English
• ISBN 10: 1461379075
• ISBN 13: 9781461379072
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 14D millimeters 235 millimeters width by 155 millimeters height by 14 millimeters depth
• Weight: 371 grams

About this title

Synopsis

The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal­ ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa­ tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera­ tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter­ disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.

Product Description

The field of optimization is interdisciplinary in nature, and has made a significant impact on many areas of technology. As a result, optimization is an indispensable tool for many practitioners in various fields. Conventional optimization techniques are well established and widely published in many excellent textbooks. However, there are new techniques, such as simulated annealing, mean field theory, and genetic algorithms, which have proven to be effective in solving global optimization problems. Computational Intelligence for Optimization is intended as a technical description of the state-of-the-art developments in advanced optimization techniques, specifically simulated annealing, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementation, and practical applications. Most of the theories covered in this work and their applications are widely scattered in journals, technical reports, and conference proceedings of various fields, making it difficult for people new in the field to find easily. The authors have brought together a comprehensive and organized treatment of these techniques, thus filling a gap in the scientific literature. Computational Intelligence for Optimization is suitable for first-year graduate courses in electrical and computer engineering, computer science, and operational research programs. It may also be used as a reference work for practising engineers, scientists, operational researchers and other specialists.