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Nature-inspired optimizers = theorie...

Mirjalili, Seyedali.

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Nature-inspired optimizers = theories, literature reviews and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature-inspired optimizers/ edited by Seyedali Mirjalili, Jin Song Dong, Andrew Lewis.
Reminder of title:	theories, literature reviews and applications /
other author:	Mirjalili, Seyedali.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xvi, 238 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Preface -- Chapter 1. Introduction to Nature-inspired Algorithms -- Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning -- Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network -- Chapter 4. Dragonﬂy Algorithm: Theory, Literature Review, and Application in Feature Selection -- Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.
Contained By:	Springer eBooks
Subject:	Evolutionary computation. -
Online resource:	https://doi.org/10.1007/978-3-030-12127-3
ISBN:	9783030121273

Nature-inspired optimizers = theories, literature reviews and applications /
Nature-inspired optimizerstheories, literature reviews and applications /[electronic resource] :edited by Seyedali Mirjalili, Jin Song Dong, Andrew Lewis. - Cham :Springer International Publishing :2020. - xvi, 238 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8111860-949X ;. - Studies in computational intelligence ;v.811..

Preface -- Chapter 1. Introduction to Nature-inspired Algorithms -- Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning -- Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network -- Chapter 4. Dragonﬂy Algorithm: Theory, Literature Review, and Application in Feature Selection -- Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.

ISBN: 9783030121273

Standard No.: 10.1007/978-3-030-12127-3doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: TA347.E96 / N388 2020

Dewey Class. No.: 006.3823

Nature-inspired optimizers = theories, literature reviews and applications /
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300 $a xvi, 238 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.811
505 0 $a Preface -- Chapter 1. Introduction to Nature-inspired Algorithms -- Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning -- Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network -- Chapter 4. Dragonﬂy Algorithm: Theory, Literature Review, and Application in Feature Selection -- Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.
520 $a This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.
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