Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolut

The Nile on
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 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 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.

FORMAT
Hardcover LANGUAGE
English CONDITION
Brand New Back Cover

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, Gr

Table of Contents

Introduction.- Meta-heuristics, machine learning and deep learning methods.- Evolutionary approach to deep learning.- Machine learning approach to evolutionary computation.- Evolutionary approach to gene regulatory networks.- Conclusion.

Review

"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)

Review Quote

"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)

Feature

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

Details ISBN9811301999 Author Hitoshi Iba Publisher Springer Verlag, Singapore Year 2018 ISBN-10 9811301999 ISBN-13 9789811301995 Format Hardcover Imprint Springer Verlag, Singapore Subtitle Neuro-Evolution and Gene Regulatory Networks Place of Publication Singapore Country of Publication Singapore Pages 245 DEWEY 006.31 Publication Date 2018-06-26 Short Title Evolutionary Approach to Machine Learning and Deep Neural Networks Language English UK Release Date 2018-06-26 Audience Postgraduate, Research & Scholarly Edition Description 2018 ed. Edition 2018th Country of Origin NL Product Class Description Computing: Professional & Programming Alternative 9789811302008 Illustrations 84 Illustrations, color; 43 Illustrations, black and white
We\'ve got this

At The Nile, if you\'re looking for it, we\'ve got it.
With fast shipping, low prices, friendly service and well over a million items - you\'re bound to find what you want, at a price you\'ll love!

TheNile_Item_ID:126693003;