Theory of Evolutionary Computation - Recent Developments in Discrete Optimization - researchr publication

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Benjamin Doerr, Frank Neumann 0001, editors, Theory of Evolutionary Computation - Recent Developments in Discrete Optimization. Natural Computing Series, Springer, 2020. [doi]

Abstract is missing.

Probabilistic Tools for the Analysis of Randomized Optimization HeuristicsBenjamin Doerr. 1-87 [doi]

Drift AnalysisJohannes Lengler. 89-131 [doi]

Complexity Theory for Discrete Black-Box Optimization HeuristicsCarola Doerr. 133-212 [doi]

Parameterized Complexity Analysis of Randomized Search HeuristicsFrank Neumann 0001, Andrew M. Sutton. 213-248 [doi]

Analysing Stochastic Search Heuristics Operating on a Fixed BudgetThomas Jansen 0001. 249-270 [doi]

Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter ChoicesBenjamin Doerr, Carola Doerr. 271-321 [doi]

Analysis of Evolutionary Algorithms in Dynamic and Stochastic EnvironmentsFrank Neumann 0001, Mojgan Pourhassan, Vahid Roostapour. 323-357 [doi]

The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime AnalysesDirk Sudholt. 359-404 [doi]

Theory of Estimation-of-Distribution AlgorithmsMartin S. Krejca, Carsten Witt. 405-442 [doi]

Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for OptimizationChristine Zarges. 443-474 [doi]

Computational Complexity Analysis of Genetic ProgrammingAndrei Lissovoi, Pietro S. Oliveto. 475-518 [doi]

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