Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks

Author:   Hitoshi Iba
Publisher:   Springer Verlag, Singapore
Edition:   1st ed. 2018
ISBN:  

9789811301995


Pages:   245
Publication Date:   26 June 2018
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks


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Author:   Hitoshi Iba
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2018
Weight:   0.559kg
ISBN:  

9789811301995


ISBN 10:   9811301999
Pages:   245
Publication Date:   26 June 2018
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Reviews

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)


Author Information

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 (ETL) in Ibaraki, Japan. He has been with The University of Tokyo since 1998 and is currently a professor at the Graduate School of Information and Communication Engineering there. His research interests include evolutionary computation, genetic programming, bioinformatics, foundations of artificial intelligence, artificial life, complex systems, and robotics.

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