Yew-Soon Ong

Jonathan H. Chan

Sung-Bae Cho

First International Conference, CSBio 2010, Bangkok, Thailand, November 3-5, 2010, Proceedings

Computational Systems-Biology and Bioinformatics

Hisao Ishibuchi

Hisashi Handa

Kay-Chen Tan

This book contains a collection of the papers accepted in the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), which was held in Singapore from 10-12th November 2014. The papers contained in this book demonstrate notable intelligent systems with good analytical and/or empirical results.

Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2

Kay Chen Tan

Chi-Keong Goh

The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized 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 Memetic algorithms and multi-objective optimization.

Multi-Objective Memetic Algorithms

Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems

Yaochu Jin

Shengxiang Yang

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.

Evolutionary Computation in Dynamic and Uncertain Environments

Yew-Soon Ong

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