Advertisement

Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments

  • Frank NeumannEmail author
  • Mojgan Pourhassan
  • Vahid Roostapour
Chapter
Part of the Natural Computing Series book series (NCS)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Frank Neumann
    • 1
    Email author
  • Mojgan Pourhassan
    • 1
  • Vahid Roostapour
    • 1
  1. 1.Optimisation and Logistics, School of Computer ScienceThe University of AdelaideAdelaideAustralia

Personalised recommendations