This paper introduces a genetic algorithm enhanced with a trap escaping strategy derived from the dual information presented as discrete Lagrange multipliers. When the genetic algorithm is trapped into a local optima, the Discrete Lagrange Multiplier method is called for the best individual found. The information provided by the Lagrangian method is unified, in the form of recombination, with the one from the last population of the genetic algorithm. Then the genetic algorithm is restarted with this new improved configuration. The proposed algorithm is tested on the winner determination problem. Experiments are conducted using instances generated with the combinatorial auction test suite system. The results show that the method is viable.
This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE