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Swarm intelligence and evolutionary computation : = theory, advances and applications in machine learning and deep learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Swarm intelligence and evolutionary computation :/ Georgios N. Kouziokas, Lecturer, School of Engineering, University of Thessaly, Greece.
其他題名:	theory, advances and applications in machine learning and deep learning /
其他題名:	Theory, advances and applications in machine learning and deep learning
作者:	Kouziokas, Georgios N.
出版者:	Boca Raton, FL :CRC Press, Taylor & Francis Group, : c2023.,
面頁冊數:	xiii, 203 p. :ill. ; : 25 cm.;
標題:	Swarm intelligence - Congresses. -
ISBN:	9781032162508 (hbk) :

Swarm intelligence and evolutionary computation : = theory, advances and applications in machine learning and deep learning /
Kouziokas, Georgios N.

Swarm intelligence and evolutionary computation :theory, advances and applications in machine learning and deep learning /Theory, advances and applications in machine learning and deep learningGeorgios N. Kouziokas, Lecturer, School of Engineering, University of Thessaly, Greece. - 1st ed. - Boca Raton, FL :CRC Press, Taylor & Francis Group,c2023. - xiii, 203 p. :ill. ;25 cm.

Includes bibliographical references and index.

1. Computational optimization -- 2. Evolutionary Computation and Genetic Algorithm -- 3. Swarm Intelligence and Particle Swarm Optimization -- 4. Ant Colony Optimization and Artificial Bee Colony -- 5. Cuckoo Search and Bat Swarm Algorithm -- 6. Firefly Algorithm, Harmony Search and Cat Swarm Algorithm -- 7. Grey Wolf, Whale and Grasshopper Optimization -- 8. Machine Learning Optimization Applications -- 9. Swarm and Evolutionary Intelligence in Deep Learning.

"The aim of this book is to present and analyze theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It includes ten relevant chapters. In chapter 1, a theoretical introduction of the computational optimization techniques is provided regarding the gradient based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. In chapter 2, evolutionary computation techniques and genetic algorithm are discussed. In chapter 3, swarm intelligence theory and particle swarm optimization algorithm are discussed. Also, several variations of particle swarm optimization algorithm are analyzed and explained such as Geometric PSO and Quantum mechanics-based PSO Algorithm. In chapter 4, two essential colony bio-inspired algorithms are examined: Ant colony optimization (ACO) and Artificial Bee Colony (ABC). In chapter 5, Cuckoo search and Bat swarm algorithms are presented and analyzed. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO). The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA). Machine learning optimization applications are presented in chapter 8, such as artificial neural network optimization. In chapter 9 an application of swarm intelligence in deep long short-term memory (LSTM) networks is discussed. In chapter 10, an illustrative application of swarm intelligence on Deep CNN satellite image classification regarding the remote sensing of environment is presented. The final scope of the book is to provide knowledge towards the application of improved optimization techniques in several computational and artificial intelligence problems"--

ISBN: 9781032162508 (hbk) :NT5233

LCCN: 2022016188Subjects--Topical Terms:

674572
Swarm intelligence
--Congresses.

LC Class. No.: Q337.3 / .K68 2023

Dewey Class. No.: 006.3/824 / K88

Swarm intelligence and evolutionary computation : = theory, advances and applications in machine learning and deep learning /
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100 1 $a Kouziokas, Georgios N. $3 1451835
245 1 0 $a Swarm intelligence and evolutionary computation : $b theory, advances and applications in machine learning and deep learning / $c Georgios N. Kouziokas, Lecturer, School of Engineering, University of Thessaly, Greece.
246 3 0 $a Theory, advances and applications in machine learning and deep learning
250 $a 1st ed.
260 # $a Boca Raton, FL : $b CRC Press, Taylor & Francis Group, $c c2023.
300 $a xiii, 203 p. : $b ill. ; $c 25 cm.
504 $a Includes bibliographical references and index.
505 0 # $a 1. Computational optimization -- 2. Evolutionary Computation and Genetic Algorithm -- 3. Swarm Intelligence and Particle Swarm Optimization -- 4. Ant Colony Optimization and Artificial Bee Colony -- 5. Cuckoo Search and Bat Swarm Algorithm -- 6. Firefly Algorithm, Harmony Search and Cat Swarm Algorithm -- 7. Grey Wolf, Whale and Grasshopper Optimization -- 8. Machine Learning Optimization Applications -- 9. Swarm and Evolutionary Intelligence in Deep Learning.
520 # $a "The aim of this book is to present and analyze theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It includes ten relevant chapters. In chapter 1, a theoretical introduction of the computational optimization techniques is provided regarding the gradient based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. In chapter 2, evolutionary computation techniques and genetic algorithm are discussed. In chapter 3, swarm intelligence theory and particle swarm optimization algorithm are discussed. Also, several variations of particle swarm optimization algorithm are analyzed and explained such as Geometric PSO and Quantum mechanics-based PSO Algorithm. In chapter 4, two essential colony bio-inspired algorithms are examined: Ant colony optimization (ACO) and Artificial Bee Colony (ABC). In chapter 5, Cuckoo search and Bat swarm algorithms are presented and analyzed. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO). The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA). Machine learning optimization applications are presented in chapter 8, such as artificial neural network optimization. In chapter 9 an application of swarm intelligence in deep long short-term memory (LSTM) networks is discussed. In chapter 10, an illustrative application of swarm intelligence on Deep CNN satellite image classification regarding the remote sensing of environment is presented. The final scope of the book is to provide knowledge towards the application of improved optimization techniques in several computational and artificial intelligence problems"-- $c Provided by publisher.
650 # 7 $a Swarm intelligence $v Congresses. $3 674572 $3 727870
650 # 7 $a Evolutionary computation $v Congresses. $3 674859 $3 727789
650 # 0 $a Deep learning (Machine learning) $3 1381171
650 # 0 $a Evolutionary computation. $3 573187
650 # 0 $a Swarm intelligence. $3 560714