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Evolutionary algorithms and neural n...

Mirjalili, Seyedali.

Evolutionary algorithms and neural networkstheory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary algorithms and neural networksby Seyedali Mirjalili.
Reminder of title:	theory and applications /
Author:	Mirjalili, Seyedali.
Published:	Cham :Springer International Publishing :2019.
Description:	xiv, 156 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	http://dx.doi.org/10.1007/978-3-319-93025-1
ISBN:	9783319930251$q(electronic bk.)

Evolutionary algorithms and neural networkstheory and applications /
Mirjalili, Seyedali.

Evolutionary algorithms and neural networkstheory and applications /[electronic resource] :by Seyedali Mirjalili. - Cham :Springer International Publishing :2019. - xiv, 156 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7801860-949X ;. - Studies in computational intelligence ;v. 216..

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$q(electronic bk.)

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

181982
Neural networks (Computer science)

LC Class. No.: QA76.87 / .M575 2019

Dewey Class. No.: 006.32

Evolutionary algorithms and neural networkstheory and applications /
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245 1 0 $a Evolutionary algorithms and neural networks $h [electronic resource] : $b theory and applications / $c by Seyedali Mirjalili.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiv, 156 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v 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.
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650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Simulation and Modeling. $3 273719
710 2 $a SpringerLink (Online service) $3 273601
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830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
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