000			03712nam a22005295i 4500
001			978-81-322-2184-5
003			DE-He213
005			20200420221256.0
007			cr nn 008mamaa
008			141213s2015 ii | s |||| 0|eng d
020			_a9788132221845 _9978-81-322-2184-5
024	7		_a10.1007/978-81-322-2184-5 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
245	1	0	_aEvolutionary Constrained Optimization _h[electronic resource] / _cedited by Rituparna Datta, Kalyanmoy Deb.
264		1	_aNew Delhi : _bSpringer India : _bImprint: Springer, _c2015.
300			_aXVI, 319 p. 111 illus., 39 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInfosys Science Foundation Series, _x2363-6149
505	0		_aA Critical Review of Adaptive Penalty Techniques in Evolutionary Computation -- Ruggedness Quantifying for Constrained Continuous Fitness Landscapes -- Trust Regions in Surrogate-Assisted Evolutionary Programming for Constrained Expensive Black-Box Optimization -- Ephemeral Resource Constraints in Optimization -- Incremental Approximation Models for Constrained Evolutionary Optimization -- Efficient Constrained Optimization by the (Sf(B Constrained Differential Evolution with Rough Approximation -- Analyzing the Behaviour of Multi-Recombinative Evolution Strategies Applied to a Conically Constrained Problem -- Locating Potentially Disjoint Feasible Regions of a Search Space with a Particle Swarm Optimizer -- Ensemble of Constraint Handling Techniques for Single Objective Constrained Optimization -- Evolutionary Constrained Optimization: A Hybrid Approach.
520			_aThis book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aMathematical optimization.
650		0	_aComputational intelligence.
650		0	_aMechanical engineering.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aMechanical Engineering.
650	2	4	_aOptimization.
700	1		_aDatta, Rituparna. _eeditor.
700	1		_aDeb, Kalyanmoy. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9788132221838
830		0	_aInfosys Science Foundation Series, _x2363-6149
856	4	0	_uhttp://dx.doi.org/10.1007/978-81-322-2184-5
912			_aZDB-2-ENG
942			_cEBK
999			_c52900 _d52900