Tabu Programming: a New Problem Solver through Adaptive Memory Programming over Tree Data Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{journals/ijitdm/HedarMF11,
  author =       "Abdel-Rahman Hedar and Emad Mabrouk and 
                 Masao Fukushima",
  title =        "Tabu Programming: a New Problem Solver through
                 Adaptive Memory Programming over Tree Data Structures",
  journal =      "International Journal of Information Technology and
                 Decision Making",
  volume =       "10",
  number =       "2",
  year =         "2011",
  pages =        "373--406",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1142/S0219622011004373",
  oai =          "oai:RePEc:wsi:ijitdm:v:10:y:2011:i:02:p:373-406",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Since the first appearance of the Genetic Programming
                 (GP) algorithm, extensive theoretical and application
                 studies on it have been conducted. Nowadays, the GP
                 algorithm is considered one of the most important tools
                 in Artificial Intelligence (AI). Nevertheless, several
                 questions have been raised about the complexity of the
                 GP algorithm and the disruption effect of the crossover
                 and mutation operators. In this paper, the Tabu
                 Programming (TP) algorithm is proposed to employ the
                 search strategy of the classical Tabu Search algorithm
                 with the tree data structure. Moreover, the TP
                 algorithm exploits a set of local search procedures
                 over a tree space in order to mitigate the drawbacks of
                 the crossover and mutation operators. Extensive
                 numerical experiments are performed to study the
                 performance of the proposed algorithm for a set of
                 benchmark problems. The results of those experiments
                 show that the TP algorithm compares favourably to
                 recent versions of the GP algorithm in terms of
                 computational efforts and the rate of success. Finally,
                 we present a comprehensive framework called
                 Meta-Heuristics Programming (MHP) as general machine
                 learning tools.",
}

Genetic Programming entries for Abdel-Rahman Hedar Emad H A Mabrouk Masao Fukushima

Citations