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008 160915s2014 gw | s |||| 0|eng d
020 _a9783642332067
_9978-3-642-33206-7
024 7 _a10.1007/978-3-642-33206-7
_2doi
035 _ato000544409
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM031000
_2bisacsh
082 0 4 _a004.0151
_223
245 1 0 _aTheory and Principled Methods for the Design of Metaheuristics
_helectronic resource
_cedited by Yossi Borenstein, Alberto Moraglio.
260 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXX, 270 p. 62 illus., 16 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aNatural Computing Series,
_x1619-7127
505 0 _aNo Free Lunch Theorems: Limitations and Perspectives of Metaheuristics -- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms -- Rugged and Elementary Landscapes -- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior -- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics -- Designing an Optimal Search Algorithm with Respect to Prior Information -- The Bayesian Search Game -- Principled Design of Continuous Stochastic Search: From Theory to Practice -- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP -- Experimental Analysis of Optimization Algorithms: Tuning and Beyond -- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.
520 _aMetaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.   In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters.   With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.
650 0 _aComputer Science.
_9155490
650 0 _aInformation theory.
_9303504
650 0 _aArtificial intelligence.
_9274099
650 0 _aMathematical optimization.
_9566241
650 0 _aengineering.
_9224332
650 0 _aOperations research.
_9303058
650 1 4 _aComputer Science.
_9155490
650 2 4 _aTheory of Computation.
_9303507
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aoptimization.
_9566242
650 2 4 _aOperation Research/Decision Theory.
_9411428
700 1 _aBorenstein, Yossi.
_eeditor.
_9450945
700 1 _aMoraglio, Alberto.
_eeditor.
_9417392
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aNatural Computing Series,
_9318767
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33206-7
912 _aZDB-2-SCS
999 _c401598