KAY CHEN TAN's Books

Evolutionary Multi-Task Optimization

Foundations and Methodologies

By: Liang Feng,Abhishek Gupta,Kay Chen Tan,Yew Soon Ong

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Evolutionary Multi-Task Optimization

Foundations and Methodologies

By: Liang Feng,Abhishek Gupta,Kay Chen Tan,Yew Soon Ong

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain’s ability to generalize in optimization – particularly in population-based evolutionary algorithms – have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

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Page Count

220

Publisher

Springer Nature

Published Date

ISBN 10

9811956502

ISBN 13

9789811956508

Modern Industrial Automation Software Design

By: Lingfeng Wang,Kay CHen Tan

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Modern Industrial Automation Software Design

By: Lingfeng Wang,Kay CHen Tan

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

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Page Count

348

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471776270

ISBN 13

9780471776277

Evolutionary Multi-objective Optimization in Uncertain Environments

Issues and Algorithms

By: Chi-Keong Goh,Kay Chen Tan

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Evolutionary Multi-objective Optimization in Uncertain Environments

Issues and Algorithms

By: Chi-Keong Goh,Kay Chen Tan

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.

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Page Count

273

Publisher

Springer

Published Date

ISBN 10

3540959769

ISBN 13

9783540959762

Neural Networks: Computational Models and Applications

By: Huajin Tang,Kay Chen Tan,Zhang Yi

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Neural Networks: Computational Models and Applications

By: Huajin Tang,Kay Chen Tan,Zhang Yi

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.

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Page Count

310

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3540692258

ISBN 13

9783540692256

Evolutionary Robotics: From Algorithms To Implementations

By: Ling-feng Wang,Kay Chen Tan,Chee-meng Chew

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Evolutionary Robotics: From Algorithms To Implementations

By: Ling-feng Wang,Kay Chen Tan,Chee-meng Chew

This invaluable book comprehensively describes evolutionary robotics and computational intelligence, and how different computational intelligence techniques are applied to robotic system design. It embraces the most widely used evolutionary approaches with their merits and drawbacks, presents some related experiments for robotic behavior evolution and the results achieved, and shows promising future research directions. Clarity of explanation is emphasized such that a modest knowledge of basic evolutionary computation, digital circuits and engineering design will suffice for a thorough understanding of the material.The book is ideally suited to computer scientists, practitioners and researchers keen on computational intelligence techniques, especially the evolutionary algorithms in autonomous robotics at both the hardware and software levels.

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Page Count

267

Publisher

World Scientific

Published Date

ISBN 10

9814477524

ISBN 13

9789814477529

Evolutionary Computation and Complex Networks

By: Jing Liu,Hussein A. Abbass,Kay Chen Tan

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Evolutionary Computation and Complex Networks

By: Jing Liu,Hussein A. Abbass,Kay Chen Tan

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.

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Page Count

160

Publisher

Springer

Published Date

ISBN 10

3319600001

ISBN 13

9783319600000

Multiobjective Evolutionary Algorithms and Applications

By: Kay Chen Tan,Eik Fun Khor,Tong Heng Lee

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Multiobjective Evolutionary Algorithms and Applications

By: Kay Chen Tan,Eik Fun Khor,Tong Heng Lee

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.

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