Analysis of randomised search heuristics for dynamic optimisation

Thomas Jansen, Christine Zarges

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Dynamic optimisation is an area of application where randomised search heuristics like evolutionary algorithms and artificial immune systems are often successful. The theoretical foundation of this important topic suffers from a lack of a generally accepted analytical framework as well as a lack of widely accepted example problems. This article tackles both problems by discussing necessary conditions for useful and practically relevant theoretical analysis as well as introducing a concrete family of dynamic example problems that draws inspiration from a well-known static example problem and exhibits a bi-stable dynamic. After the stage has been set this way, the framework is made concrete by presenting the results of thorough theoretical and statistical analysis for mutation-based evolutionary algorithms and artificial immune systems.

Original languageEnglish
Pages (from-to)513-541
Number of pages29
JournalEvolutionary Computation
Issue number4
Publication statusPublished - 1 Dec 2015


  • Artificial immune systems
  • Dynamic optimisation problems
  • Evolutionary algorithms
  • Fixed budget computations
  • Theory

ASJC Scopus subject areas

  • Computational Mathematics


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