Analysis of evolutionary algorithms in dynamic and stochastic environments
Date
2020
Authors
Neumann, F.
Pourhassan, M.
Roostapour, V.
Editors
Doerr, B.
Neumann, F.
Neumann, F.
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Book chapter
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
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Frank Neumann, Mojgan Pourhassan and Vahid Roostapour
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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.
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© Springer Nature Switzerland AG 2020