Landscape State Machines: Tools for Evolutionary Algorithm Performance Analyses and Landscape/Algorithm Mapping

  • David Corne
  • Martin Oates
  • Douglas Kell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the ‘real’ landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutationonly algorithms, but there are various ways to address this and other limitations.


Mutation Operator Finite State Machine Algorithm Performance Fitness Evaluation Fitness Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • David Corne
    • 1
  • Martin Oates
    • 1
    • 2
  • Douglas Kell
    • 3
  1. 1.School of Systems EngineeringUniversity of ReadingReadingUK
  2. 2.Evosolve LtdStowmarketUK
  3. 3.Department of ChemistryUMISTManchesterUK

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