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Begins with the essentials of evolutionary algorithms and covers state-of-the-art research methodologies in the field as well as growing research trends
Presents concepts to promote and facilitate effective research in evolutionary algorithm approaches both in theory and in practice
Inspires readers to explore the rapidly developing fields of artificial intelligence and evolutionary algorithms, actively introducing research topics in those areas
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
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.
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Authors and Affiliations
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The University of Tokyo, Tokyo, Japan
Hitoshi Iba
About the author
Bibliographic Information
Book Title: Evolutionary Approach to Machine Learning and Deep Neural Networks
Book Subtitle: Neuro-Evolution and Gene Regulatory Networks
Authors: Hitoshi Iba
DOI: https://doi.org/10.1007/978-981-13-0200-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-13-0199-5Published: 26 June 2018
Softcover ISBN: 978-981-13-4358-2Published: 01 February 2019
eBook ISBN: 978-981-13-0200-8Published: 15 June 2018
Edition Number: 1
Number of Pages: XIII, 245
Number of Illustrations: 43 b/w illustrations, 84 illustrations in colour
Topics: Artificial Intelligence, Bioinformatics, Mathematical and Computational Biology, Computational Intelligence