000			04040cam a2200541 i 4500
001			9781003247746
003			FlBoTFG
005			20240213122833.0
006			m o d
007			cr cnu---unuuu
008			230126s2022 flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003247746 _q(electronic bk.)
020			_a1003247741 _q(electronic bk.)
020			_a9781000846164 _q(electronic bk. : PDF)
020			_a1000846164 _q(electronic bk. : PDF)
020			_a9781000846201 _q(electronic bk. : EPUB)
020			_a1000846202 _q(electronic bk. : EPUB)
020			_z9781032162508
020			_z1032162503
024	7		_a10.1201/9781003247746 _2doi
035			_a(OCoLC)1365637367
035			_a(OCoLC-P)1365637367
050		4	_aQ337.3
072		7	_aCOM _x037000 _2bisacsh
072		7	_aCOM _x059000 _2bisacsh
072		7	_aMAT _x003000 _2bisacsh
072		7	_aUYQ _2bicssc
082	0	4	_a006.3/824 _223/eng/20230207
100	1		_aKouziokas, Georgios N., _eauthor.
245	1	0	_aSwarm intelligence and evolutionary computation : _btheory, advances and applications in machine learning and deep learning / _cGeorgios N. Kouziokas, Lecturer, School of Engineering, University of Thessaly, Greece.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2022.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
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"-- _cProvided by publisher.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aSwarm intelligence.
650		0	_aEvolutionary computation.
650		0	_aDeep learning (Machine learning)
650		7	_aCOMPUTERS / Machine Theory _2bisacsh
650		7	_aCOMPUTERS / Computer Engineering _2bisacsh
650		7	_aMATHEMATICS / Applied _2bisacsh
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003247746
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c6138 _d6138