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Theory of Evolutionary Computation 2020

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Benjamin Doerr, Frank Neumann:
Theory of Evolutionary Computation - Recent Developments in Discrete Optimization. Natural Computing Series, Springer 2020, ISBN 978-3-030-29413-7
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Benjamin Doerr:
Probabilistic Tools for the Analysis of Randomized Optimization Heuristics. 1-87
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  - series/ncs/Lengler20
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Johannes Lengler:
Drift Analysis. 89-131
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Carola Doerr:
Complexity Theory for Discrete Black-Box Optimization Heuristics. 133-212
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Frank Neumann, Andrew M. Sutton:
Parameterized Complexity Analysis of Randomized Search Heuristics. 213-248
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Thomas Jansen:
Analysing Stochastic Search Heuristics Operating on a Fixed Budget. 249-270
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Benjamin Doerr, Carola Doerr:
Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices. 271-321
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Frank Neumann, Mojgan Pourhassan, Vahid Roostapour:
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments. 323-357
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Dirk Sudholt:
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. 359-404
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  - series/ncs/KrejcaW20
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Martin S. Krejca, Carsten Witt:
Theory of Estimation-of-Distribution Algorithms. 405-442
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Christine Zarges:
Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization. 443-474
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Andrei Lissovoi, Pietro S. Oliveto:
Computational Complexity Analysis of Genetic Programming. 475-518

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