• DocumentCode
    2917417
  • Title

    Analysis and extension of the Inc* on the satisfiability testing problem

  • Author

    Bader-El-Den, Mohamed ; Poli, Riccardo

  • Author_Institution
    Dept. of Comput. & Electron. Syst., Univ. of Essex, Colchester
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    3342
  • Lastpage
    3349
  • Abstract
    Inc* is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithmpsilas behaviour.
  • Keywords
    computational complexity; genetic algorithms; search problems; Inc; genetic programming; local search heuristic; satisfiability testing problem; Algorithm design and analysis; Benchmark testing; Gain; Genetic programming; Heuristic algorithms; Labeling; Random variables; Traveling salesman problems;
  • 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.2008.4631250
  • Filename
    4631250