• DocumentCode
    2915263
  • Title

    Combinatorial landscape analysis for k-SAT instances

  • Author

    Albrecht, Andreas A. ; Lane, Peter C R ; Steinhöfel, Kathleen

  • Author_Institution
    Sch. of Comput. Sci., Hertfordshire Univ., Hatfield
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    2498
  • Lastpage
    2504
  • Abstract
    Over the past ten years, methods from statistical physics have provided a deeper inside into the average complexity of hard combinatorial problems, culminating in a rigorous proof for the asymptotic behaviour of the k-SAT phase transition threshold by Achlioptas and Peres in 2004. On the other hand, when dealing with individual instances of hard problems, gathering information about specific properties of instances in a pre-processing phase might be helpful for an appropriate adjustment of local search-based procedures. In the present paper, we address both issues in the context of landscapes induced by k-SAT instances: Firstly, we utilize a sampling method devised by Garnier and Kallel in 2002 for approximations of the number of local maxima in landscapes generated by individual k-SAT instances and a simple neighbourhood relation. The objective function is given by the number of satisfied clauses. Secondly, we outline a method for obtaining upper bounds for the average number of local maxima in k-SAT instances which indicates some kind of phase transition for the neighbourhood-specific ratio m/n = Theta(2k/k).
  • Keywords
    combinatorial mathematics; optimisation; search problems; combinatorial landscape analysis; hard combinatorial problems; k-SAT phase transition; local search-based procedures; neighbourhood-specific ratio; objective function; phase transition; Algorithm design and analysis; Computer science; Optimization methods; Phase estimation; Physics; Proteins; Runtime; Sampling methods; Search methods; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-1822-0
  • Electronic_ISBN
    978-1-4244-1823-7
  • Type

    conf

  • DOI
    10.1109/CEC.2008.4631133
  • Filename
    4631133