<ul>
<li>The main subjects in this book relate to software development using cutting-edge technologies for real-world industrial automation applications</li> <li>A hands-on approach to applying a wide variety of emerging technologies to modern industrial practice problems</li> <li>Explains key concepts through clear examples, ranging from simple to more complex problem domains, and all based on real-world industrial problems</li> <li>A useful reference book for practicing engineers as well as an updated resource book for researchers</li> </ul>

Modern Industrial Automation Software Design

Computers & Technology

Tan, Kay CHen

Wang, Lingfeng

Programming

Wiley-IEEE Press

Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. Covers the authors’ recent research in the area of multiobjective evolutionary algorithms as well as its practical applications.

Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)

Lee, Tong Heng

Khor, Eik Fun

Computer Science

Springer

This book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.

Evolutionary Computation and Complex Networks

Liu, Jing

Abbass, Hussein A.

This book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.

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.

Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Studies in Computational Intelligence, 186)

Goh, Chi-Keong

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Neural Networks: Computational Models and Applications (Studies in Computational Intelligence, 53)

Yi, Zhang

Tang, Huajin

Evolutionary Multi-Task Optimization: Foundations and Methodologies (Machine Learning: Foundations, Methodologies, and Applications)

Gupta, Abhishek

Feng, Liang

Ong, Yew Soon

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Books by Tan, Kay CHen