Analysis of Randomised Search Heuristics for Dynamic Optimisation

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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 - 15 Dec 2015


  • dynamic optimisation problems
  • evolutionary algorithms
  • artificial immune systems
  • theory
  • fixed budget computations


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