國立虎尾科技大學 |

Evolutionary Algorithms and Neural Networks = Theory and Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evolutionary Algorithms and Neural Networks/ by Seyedali Mirjalili.
其他題名:	Theory and Applications /
作者:	Mirjalili, Seyedali.
面頁冊數:	XIV, 156 p. 68 illus., 60 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-93025-1
ISBN:	9783319930251

Evolutionary Algorithms and Neural Networks = Theory and Applications /
Mirjalili, Seyedali.

Evolutionary Algorithms and Neural NetworksTheory and Applications /[electronic resource] :by Seyedali Mirjalili. - 1st ed. 2019. - XIV, 156 p. 68 illus., 60 illus. in color.online resource. - Studies in Computational Intelligence,7801860-949X ;. - Studies in Computational Intelligence,564.

Part I: Evolutionary algorithms -- Introduction to Evolutionary Single-objective Optimisation -- Particle Swarm Optimisation -- Ant Colony Optimization -- Genetic Algorithm -- Biogeography-Based Optimization -- Part II: Evolutionary Neural Networks -- Evolutionary Feedforward Neural Networks -- Evolutionary Multi-Layer Perceptron -- Evolutionary Radial Basis Function Networks -- Evolutionary Deep Neural Networks.

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials. .

ISBN: 9783319930251

Standard No.: 10.1007/978-3-319-93025-1doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Evolutionary Algorithms and Neural Networks = Theory and Applications /
LDR:02935nam a22004095i 4500 001 1016436
003 DE-He213
005 20200703150212.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319930251 $9 978-3-319-93025-1
024 7 $a 10.1007/978-3-319-93025-1 $2 doi
035 $a 978-3-319-93025-1
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Mirjalili, Seyedali. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1310847
245 1 0 $a Evolutionary Algorithms and Neural Networks $h [electronic resource] : $b Theory and Applications / $c by Seyedali Mirjalili.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIV, 156 p. 68 illus., 60 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 780
505 0 $a Part I: Evolutionary algorithms -- Introduction to Evolutionary Single-objective Optimisation -- Particle Swarm Optimisation -- Ant Colony Optimization -- Genetic Algorithm -- Biogeography-Based Optimization -- Part II: Evolutionary Neural Networks -- Evolutionary Feedforward Neural Networks -- Evolutionary Multi-Layer Perceptron -- Evolutionary Radial Basis Function Networks -- Evolutionary Deep Neural Networks.
520 $a This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials. .
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Neural networks (Computer science) . $3 1253765
650 0 $a Computer simulation. $3 560190
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
650 2 4 $a Simulation and Modeling. $3 669249
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319930244
776 0 8 $i Printed edition: $z 9783319930268
776 0 8 $i Printed edition: $z 9783030065720
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-319-93025-1
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)