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 under various noise models and point out some
directions for future research.Comment: This book chapter is to appear in the book "Theory of Randomized
Search Heuristics in Discrete Search Spaces", which is edited by Benjamin
Doerr and Frank Neumann and is scheduled to be published by Springer in 201