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The Algorithm Selection Problem on the Continuous Optimization Domain

  • Mario A. Muñoz
  • Michael Kirley
  • Saman K. Halgamuge
Part of the Studies in Computational Intelligence book series (SCI, volume 445)

Abstract

The problem of algorithm selection, that is identifying the most efficient algorithm for a given computational task, is non-trivial. Meta-learning techniques have been used successfully for this problem in particular domains, including pattern recognition and constraint satisfaction. However, there has been a paucity of studies focused specifically on algorithm selection for continuous optimization problems. This may be attributed to some extent to the difficulties associated with quantifying problem “hardness” in terms of the underlying cost function. In this paper, we provide a survey of the related literature in the continuous optimization domain. We discuss alternative approaches for landscape analysis, algorithm modeling and portfolio development. Finally, we propose a meta-learning framework for the algorithm selection problem in the continuous optimization domain.

Keywords

Algorithm Selection Fitness Landscape Continuous Optimization Combinatorial Auction Landscape Analysis 
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 2013

Authors and Affiliations

  • Mario A. Muñoz
    • 1
  • Michael Kirley
    • 2
  • Saman K. Halgamuge
    • 1
  1. 1.Department of Mechanical EngineeringThe University of MelbourneParkvilleAustralia
  2. 2.Department of Computing and Information SystemsThe University of MelbourneParkvilleAustralia

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