Improving Tabu Search Performance by Means of Automatic Parameter Tuning

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Carolina Lagos and Broderick Crawford and 
                 Ricardo Soto and Enrique Cabrera and Jorge Vega and 
                 Franklin Johnson and Fernando Paredes",
  journal =      "Canadian Journal of Electrical and Computer
  title =        "Improving Tabu Search Performance by Means of
                 Automatic Parameter Tuning",
  year =         "2016",
  volume =       "39",
  number =       "1",
  pages =        "51--58",
  abstract =     "A common problem when performing (meta)heuristic
                 techniques over complex combinatorial optimisation
                 problems is parameter tuning. Finding the right
                 parameter values can lead to significant improvements
                 in terms of the best solution objective value found by
                 the heuristic, heuristic reliability, and heuristic
                 convergence, among others. Unfortunately, this is
                 usually a tedious and complicated task if done
                 manually. Furthermore, parameter values usually depend
                 on the problem that is going to be solved. In this
                 paper, we propose a framework that is based on the
                 genetic programming (GP) technique to fine tune a key
                 parameter of the well-known tabu search (TS) algorithm.
                 Several experiments are performed over a set of small
                 instances of the well-known capacitated facility
                 location problem. The results have shown that adjusting
                 the probability of acceptance of the best neighbour ρ
                 in the TS algorithm using GP leads to an average value
                 of the obtained solution that is closer to the optimal
                 solution than the average value obtained by the simple
                 TS algorithm with an a priori selected value for ρ.
                 More importantly, standard deviation of the algorithm
                 is greatly improved by our approach, which makes it
                 much more reliable if time limitations are present.
                 Finally, we confirm that the value of the parameter ρ
                 largely depends on the problem that is attempted to
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CJECE.2015.2496338",
  ISSN =         "0840-8688",
  month =        "winter",
  notes =        "Also known as \cite{7429013}",

Genetic Programming entries for Carolina Lagos Broderick Crawford Ricardo Soto Enrique Cabrera Jorge Vega Franklin Johnson Fernando Paredes