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001 978-1-84996-129-5
003 DE-He213
005 20140220084515.0
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008 100609s2010 xxk| s |||| 0|eng d
020 _a9781849961295
_9978-1-84996-129-5
024 7 _a10.1007/978-1-84996-129-5
_2doi
050 4 _aQA76.9.M35
072 7 _aGPFC
_2bicssc
072 7 _aTEC000000
_2bisacsh
082 0 4 _a620
_223
100 1 _aYu, Xinjie.
_eauthor.
245 1 0 _aIntroduction to Evolutionary Algorithms
_h[electronic resource] /
_cby Xinjie Yu, Mitsuo Gen.
264 1 _aLondon :
_bSpringer London,
_c2010.
300 _aXVII, 418p. 168 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aDecision Engineering,
_x1619-5736 ;
_v0
505 0 _aEvolutionary Algorithms -- Simple Evolutionary Algorithms -- Advanced Evolutionary Algorithms -- Dealing with Complicated Problems -- Constrained Optimization -- Multimodal Optimization -- Multiobjective Optimization -- Combinatorial Optimization -- Brief Introduction to Other Evolutionary Algorithms -- Swarm Intelligence -- Artificial Immune Systems -- Genetic Programming.
520 _aEvolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 0 _aPhysics.
650 0 _aControl engineering systems.
650 1 4 _aEngineering.
650 2 4 _aComplexity.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aControl , Robotics, Mechatronics.
650 2 4 _aSimulation and Modeling.
700 1 _aGen, Mitsuo.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781849961288
830 0 _aDecision Engineering,
_x1619-5736 ;
_v0
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84996-129-5
912 _aZDB-2-ENG
999 _c110975
_d110975