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A Linear Constrained Optimization Benchmark for Probabilistic Search Algorithms: The Rotated Klee-Minty Problem

  • Michael Hellwig
  • Hans-Georg Beyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking. Regarding the domain of constrained optimization, the small number of currently available benchmark environments bears no relation to the vast number of distinct problem features. The present paper advances a proposal of a scalable linear constrained optimization problem that is suitable for benchmarking Evolutionary Algorithms. By comparing two recent Evolutionary Algorithm variants, the linear benchmarking environment is demonstrated.

Keywords

Probabilistic search algorithms Benchmarking Evolutionary algorithms Linear problems Constrained optimization Klee-Minty cube 

Notes

Acknowledgements

This work was supported by the Austrian Science Fund (FWF) under grant P29651-N32. The article is also based upon work from COST Action CA15140 supported by COST (European Cooperation in Science and Technology).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Vorarlberg University of Applied Sciences, Research Centre PPEDornbirnAustria

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