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https://hdl.handle.net/2440/131758
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Type: | Book chapter |
Title: | Analysis of evolutionary algorithms in dynamic and stochastic environments |
Author: | Neumann, F. Pourhassan, M. Roostapour, V. |
Citation: | Theory of Evolutionary Computation: Recent Developments in Discrete Optimization, 2020 / Doerr, B., Neumann, F. (ed./s), vol.abs/1806.08547, Ch.7, pp.323-358 |
Publisher: | Springer |
Publisher Place: | Cham, Switzerland |
Issue Date: | 2020 |
Series/Report no.: | Natural Computing Series |
ISBN: | 3030294137 9783030294137 |
Editor: | Doerr, B. Neumann, F. |
Statement of Responsibility: | Frank Neumann, Mojgan Pourhassan and Vahid Roostapour |
Abstract: | Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems. This survey gives an overview of major theoretical developments in the area of runtime analysis for these problems. We review recent theoretical studies of evolutionary algorithms and ant colony optimization for problems where the objective functions or the constraints change over time. Furthermore, we consider stochastic problems with various noise models and point out some directions for future research. |
Rights: | © Springer Nature Switzerland AG 2020 |
DOI: | 10.1007/978-3-030-29414-4_7 |
Published version: | https://link.springer.com/book/10.1007/978-3-030-29414-4 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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