000			02592cam a22003375a 4500
001			ssj0001558339
003			OSt
005			20160424131140.0
008			150701t2015 gw a frb 001 0 eng d
020			_a9783662448731
040			_dWaSeSS _dEG-ScBUE _beng
082	0	4	_a006.3 _222 _bEIB
100	1		_aEiben, Agoston E. _939889
245	1	0	_aIntroduction to evolutionary computing / _cA. E. Eiben, J. E. Smith.
250			_a2nd ed.
260			_aHeidelberg : _bSpringer, _cc.2015.
300			_axii, 287 p. : _bill. ; _c24 cm.
490	0		_aNatural Computing Series, _x1619-7127.
500			_aIndex : p. 283-287.
504			_aBibliography : p. 259-282.
505	0		_aProblems to Be Solved -- Evolutionary Computing: The Origins -- What Is an Evolutionary Algorithm? -- Representation, Mutation, and Recombination -- Fitness, Selection, and Population Management -- Popular Evolutionary Algorithm Variants -- Hybridisation with Other Techniques: Memetic Algorithms -- Nonstationary and Noisy Function Optimisation -- Multiobjective Evolutionary Algorithms -- Constraint Handling -- Interactive Evolutionary Algorithms -- Coevolutionary Systems -- Theory -- Evolutionary Robotics -- Parameters and Parameter Tuning -- Parameter Control -- Working with Evolutionary Algorithms -- References.
506			_aAvailable on campus and off campus with authorized login.
520			_aThe overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
650		7	_aEvolutionary computation. _939890 _2BUEsh
650		7	_aEvolutionary programming (Computer science) _939891 _2BUEsh
651			_2BUEsh
653			_bCOMSCI _cApril2016
700	1		_aSmith, J. E.
710	2		_aSpringerLink (Online service)
942			_2ddc
999			_c21675 _d21647