# Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments

arXiv: https://arxiv.org/abs/1806.08547 · HTML: https://mlsanity.com/paper/1806.08547/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees](https://mlsanity.com/paper/2110.09332/) (2021)
- [Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise](https://mlsanity.com/paper/2305.10259/) (2023)
- [Dynamic Quality-Diversity Search](https://mlsanity.com/paper/2404.05769/) (2024)

## Abstract (arXiv)

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.