title: Analysis of Randomised Search Heuristics for Dynamic Optimisation.
abstract: 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.
authors.affiliation: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK t.jansen@aber.ac.uk.
authors.forename: Thomas
authors.initials: T
authors.lastname: Jansen
authors.affiliation: School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK c.zarges@cs.bham.ac.uk.
authors.forename: Christine
authors.initials: C
authors.lastname: Zarges
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001402:B-Lymphocytes / Q000276:immunology; D005075:Biological Evolution; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D055633:Immune System Phenomena; D011897:Random Allocation; D027041:Somatic Hypermutation, Immunoglobulin
keywords: Dynamic optimisation problems; artificial immune systems; evolutionary algorithms; fixed budget computations; theory