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Nature-Inspired Optimizers

Editat de Seyedali Mirjalili, Jin Song Dong, Andrew Lewis
en Limba Engleză Paperback – 28 oct 2020
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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Specificații

ISBN-13: 9783030121297
ISBN-10: 3030121291
Pagini: 256
Ilustrații: XVI, 238 p. 108 illus., 101 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:1st ed. 2020
Editura: Springer
Locul publicării:Cham, Switzerland

Cuprins

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

Textul de pe ultima copertă

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.

Caracteristici

Offers a comprehensive tutorial on evolutionary optimization Describes the mathematical model each algorithm is based on Reports on several benchmark case studies Source codes are available on a dedicated webpage