Description: Evolutionary Approach to Machine Learning and Deep Neural Networks Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Neuro-Evolution and Gene Regulatory Networks Author(s): Hitoshi Iba Format: Paperback Publisher: Springer Verlag, Singapore, Singapore Imprint: Springer Verlag, Singapore ISBN-13: 9789811343582, 978-9811343582 Synopsis 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, Groebner 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 [url] the chapters on introduction and basic methods, Chapter 3 details a new research direction, [url] 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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Book Title: Evolutionary Approach to Machine Learning and Deep Neural Netw...
Number of Pages: 245 Pages
Language: English
Publication Name: Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks
Publisher: Springer Verlag, Singapore
Publication Year: 2019
Subject: Engineering & Technology, Computer Science, Biology, Mathematics
Item Height: 235 mm
Item Weight: 403 g
Type: Textbook
Author: Hitoshi Iba
Item Width: 155 mm
Format: Paperback