LEADER 04359nam 22006255 450 001 9910734097603321 005 20230719192542.0 010 $a3-030-29414-5 024 7 $a10.1007/978-3-030-29414-4 035 $a(CKB)4100000009845260 035 $a(DE-He213)978-3-030-29414-4 035 $a(MiAaPQ)EBC5982902 035 $a(PPN)248599429 035 $a(EXLCZ)994100000009845260 100 $a20191120d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTheory of Evolutionary Computation $eRecent Developments in Discrete Optimization /$fedited by Benjamin Doerr, Frank Neumann 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 506 p. 27 illus., 17 illus. in color.) 225 1 $aNatural Computing Series,$x2627-6461 311 $a3-030-29413-7 327 $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. 330 $aThis edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing. 410 0$aNatural Computing Series,$x2627-6461 606 $aComputer science 606 $aArtificial intelligence 606 $aMathematical optimization 606 $aOperations research 606 $aTheory of Computation 606 $aArtificial Intelligence 606 $aOptimization 606 $aOperations Research and Decision Theory 615 0$aComputer science. 615 0$aArtificial intelligence. 615 0$aMathematical optimization. 615 0$aOperations research. 615 14$aTheory of Computation. 615 24$aArtificial Intelligence. 615 24$aOptimization. 615 24$aOperations Research and Decision Theory. 676 $a004.0151 702 $aDoerr$b Benjamin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNeumann$b Frank$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734097603321 996 $aTheory of Evolutionary Computation$92129712 997 $aUNINA