Evolutionary Approach to Machine Learning and Deep Neural Networks by Hitoshi Iba

Evolutionary Approach to Machine Learning and Deep Neural Networks by Hitoshi Iba

This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.

Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.

The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

The main aim of this work is to present and elaborate the bridge between theoretical approaches and the concrete, real-life challenges in genetics... the author's efforts to present these concepts in an accessible manner brings the edge of research within the reach of a wider audience. The examples and the algebraic formalism throughout, augmented by the relevant references ... open this field to undergraduates, postgraduates and established researchers alike and provide a solid starting point to more progressive research. (Irina Ioana Mohorianu, zbMATH 1394.68003, 2018)

Hitoshi Iba received his Ph.D. degree from The University of Tokyo, Japan, in 1990. From 1990 to 1998, he was with the Electro Technical Laboratory in Ibaraki, Japan. Since 1998, he has been with The University of Tokyo, where he is currently a professor in the Graduate School of Information Science and Technology. His research interests include evolutionary computation, artificial life, artificial intelligence, and robotics. He is an associate editor of the Journal of Genetic Programming and Evolvable Machines (GPEM). Dr. Iba is also is an underwater naturalist and experienced Professional Association of Diving Instructors (PADI) divemaster, having completed more than a thousand dives.

Nasimul Noman received his Ph.D. degree from The University of Tokyo, Japan, in 2007. He was a faculty member in the Department of Computer Science and Engineering, University of Dhaka, Bangladesh, from 2002 to 2012. In 2013, he joined the School of Electrical Engineering and Computing at The University of Newcastle, Australia, and currently he is working as a senior lecturer there. His research interests include evolutionary computation, computational biology, bioinformatics, and machine learning.

SKU	Unavailable
ISBN 13	9789811343582
ISBN 10	9811343586
Title	Evolutionary Approach to Machine Learning and Deep Neural Networks
Author	Hitoshi Iba
Condition	Unavailable
Binding Type	Paperback
Publisher	Springer Verlag, Singapore
Year published	2019-02-01
Number of pages	245
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.