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001 978-3-030-29414-4
003 DE-He213
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020 _a9783030294144
_9978-3-030-29414-4
024 7 _a10.1007/978-3-030-29414-4
_2doi
050 4 _aQA75.5-76.95
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
_2thema
082 0 4 _a004.0151
_223
245 1 0 _aTheory of Evolutionary Computation
_h[electronic resource] :
_bRecent Developments in Discrete Optimization /
_cedited by Benjamin Doerr, Frank Neumann.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXII, 506 p. 27 illus., 17 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 _aNatural Computing Series,
_x2627-6461
505 0 _aProbabilistic Tools for the Analysis of Randomized Optimization Heuristics -- Drift Analysis -- Complexity Theory for Discrete Black-Box Optimization Heuristics -- Parameterized Complexity Analysis of Randomized Search Heuristics -- Analysing Stochastic Search Heuristics Operating on a Fixed Budget -- Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices -- Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments -- The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses -- Theory of Estimation-of-Distribution Algorithms -- Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization -- Computational Complexity Analysis of Genetic Programming.
520 _aThis edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aMathematical optimization.
650 0 _aOperations research.
650 1 4 _aTheory of Computation.
650 2 4 _aArtificial Intelligence.
650 2 4 _aOptimization.
650 2 4 _aOperations Research and Decision Theory.
700 1 _aDoerr, Benjamin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNeumann, Frank.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030294137
776 0 8 _iPrinted edition:
_z9783030294151
776 0 8 _iPrinted edition:
_z9783030294168
830 0 _aNatural Computing Series,
_x2627-6461
856 4 0 _uhttps://doi.org/10.1007/978-3-030-29414-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174486
_d174486