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Clarifying the Difference in Local Optima Network Sampling Algorithms

  • Sarah L. ThomsonEmail author
  • Gabriela Ochoa
  • Sébastien Verel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11452)

Abstract

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on a random-walk snowballing procedure, while the other is centred around multiple traced runs of an Iterated Local Search. Both of these are proposed for the Quadratic Assignment Problem (QAP), making this the focus of our study. It is important to note the sampling algorithm frameworks could easily be modified for other domains. In our study descriptive statistics for the obtained search space samples are contrasted and commented on. The LON features are also used in linear mixed models and random forest regression for predicting heuristic optimisation performance of two prominent heuristics for the QAP on the underlying combinatorial problems. The model results are then used to make deductions about the sampling algorithms’ utility. We also propose a specific set of LON metrics for use in future predictive models alongside previously-proposed network metrics, demonstrating the payoff in doing so.

Keywords

Combinatorial fitness landscapes Local optima networks Quadratic Assignment Problem 

Notes

Acknowledgements

This work is supported by the UK’s Engineering and Physical Sciences Research Council (grant number EP/J017515/1). Data generated during this research are available from the Stirling Online Repository for Research Data (http://hdl.handle.net/11667/91).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computing Science and MathematicsUniversity of StirlingStirlingUK
  2. 2.Université du Littoral Côte d’Opale, EA 4491 - LISICCalaisFrance

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