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Landscape Encodings Enhance Optimization

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  • Konstantin Klemm
  • Anita Mehta
  • Peter F Stadler

Abstract

Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state.

Suggested Citation

  • Konstantin Klemm & Anita Mehta & Peter F Stadler, 2012. "Landscape Encodings Enhance Optimization," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-6, April.
    • Handle: RePEc:plo:pone00:0034780
    DOI: 10.1371/journal.pone.0034780
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