000			03227nam a2200361Ii 4500
001			9780429175411
008			180727s2000 flu b ob 001 0 eng d
020			_a9780429175411 _q(e-book : PDF)
035			_a(OCoLC)1042329072
050		4	_aQA76.618 _b.E882 2000
072		7	_aCOM _x051240 _2bisacsh
072		7	_aCOM _x059000 _2bisacsh
072		7	_aTJF _2bicscc
082	0	4	_a006.3 _223
100	1		_aDumitrescu, D., _eauthor. _915708
245	1	0	_aEvolutionary computation / _cby D. Dumitrescu, Beatrice Lazzerini, Lakhmi C. Jain and A. Dumitrescu.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, an imprint of Taylor and Francis, _c2000.
300			_a1 online resource (424 pages).
490	1		_aInternational series on computational intelligence
505	0		_achapter 1 Principles of evolutionary computation -- chapter 2 Genetic algorithms -- chapter 3 Basic selection schemes in evolutionary algorithms -- chapter 4 Selection based on scaling and ranking mechanisms -- chapter 5 Further selection strategies -- chapter 6 Recombination operators within binary encoding -- chapter 7 Mutation operators and related topics -- chapter 8 Schema theorem, building blocks, and related topics -- chapter 9 Real-valued encoding -- chapter 10 Hybridization, parametersetting, and adaptation -- chapter 11 Adaptive representations: messy genetic algorithms, delta coding, and diploidic representation -- chapter 12 Evolution strategies and evolutionary programming -- chapter 13 Population models and parallel implementations -- chapter 14 Genetic programming -- chapter 15 Learning classifier systems -- chapter 16 Applications of evolutionary computation.
520	3		_aRapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation.Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.
650		0	_aEvolutionary programming (Computer science) _915709
650		7	_aCOMPUTERS / Computer Engineering. _2bisacsh _94770
700	1		_aDumitrescu, A., _eauthor. _915710
700	1		_aJain, Lakhmi C., _eauthor. _915711
700	1		_aLazzerini, Beatrice, _d1953- _eauthor. _915712
710	2		_aTaylor and Francis. _910719
776	0	8	_iPrint version: _z9780849305887 _w(DLC) 00030348
830		0	_aInternational series on computational intelligence. _915713
856	4	0	_uhttps://www.taylorfrancis.com/books/9781482273960 _zClick here to view.
942			_cEBK
999			_c71068 _d71068