On the performance of the LP-guided Hopfield network-genetic algorithm

  • Authors:

  • Sancho Salcedo-Sanz;Emilio G. Ortiz-García;Ángel M. Pérez-Bellido;Antonio Portilla-Figueras;Francisco López-Ferreras

  • Affiliations:

  • Department of Signal Theory and Communications, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2009

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Abstract

This paper proposes a linear programming (LP)-guided Hopfield-genetic algorithm for a class of combinatorial optimization problems which admit a 0-1 integer linear programming. The algorithm modifies the updating order of the binary Hopfield network in order to obtain better performance of the complete hybrid approach. We theoretically analyze several different updating orders proposed. We also include in the paper a novel proposal to guide the Hopfield network using the crossover and mutation operators of the genetic algorithm. Experimental evidences that show the good performance of the proposed approach in two different combinatorial optimization problems are also included in the paper.