國立高雄大學圖資館 |

Language: English

Back

Evolutionary approach to machine lea...

Iba, Hitoshi.

Evolutionary approach to machine learning and deep neural networksneuro-evolution and gene regulatory networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary approach to machine learning and deep neural networksby Hitoshi Iba.
Reminder of title:	neuro-evolution and gene regulatory networks /
Author:	Iba, Hitoshi.
Published:	Singapore :Springer Singapore :2018.
Description:	xiii, 245 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-981-13-0200-8
ISBN:	9789811302008$q(electronic bk.)

Evolutionary approach to machine learning and deep neural networksneuro-evolution and gene regulatory networks /
Iba, Hitoshi.

Evolutionary approach to machine learning and deep neural networksneuro-evolution and gene regulatory networks /[electronic resource] :by Hitoshi Iba. - Singapore :Springer Singapore :2018. - xiii, 245 p. :ill., digital ;24 cm.

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.

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, Grobner 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.

ISBN: 9789811302008$q(electronic bk.)

Standard No.: 10.1007/978-981-13-0200-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Evolutionary approach to machine learning and deep neural networksneuro-evolution and gene regulatory networks /
LDR:03135nmm a2200325 a 4500 001 540462
003 DE-He213
005 20180615155816.0
006 m d
007 cr nn 008maaau
008 190308s2018 si s 0 eng d
020 $a 9789811302008$q(electronic bk.)
020 $a 9789811301995$q(paper)
024 7 $a 10.1007/978-981-13-0200-8 $2 doi
035 $a 978-981-13-0200-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .I12 2018
100 1 $a Iba, Hitoshi. $3 259967
245 1 0 $a Evolutionary approach to machine learning and deep neural networks $h [electronic resource] : $b neuro-evolution and gene regulatory networks / $c by Hitoshi Iba.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2018.
300 $a xiii, 245 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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, Grobner 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.
650 0 $a Machine learning. $3 188639
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Evolutionary computation. $3 231709
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Bioinformatics. $3 194415
650 2 4 $a Mathematical and Computational Biology. $3 514442
650 2 4 $a Computational Intelligence. $3 338479
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-13-0200-8
950 $a Computer Science (Springer-11645)