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Landscape encodings enhance optimization

By Konstantin Klemm, Anita Mehta and Peter F. Stadler


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.Comment: 8 pages, 3 figure

Topics: Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Quantitative Biology - Populations and Evolution
Year: 2012
DOI identifier: 10.1371/journal.pone.0034780
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