Real-Coded Genetic Algorithms Based on Mathematical Morphology

Abstract

The goal of this work is to propose a general-purpose crossover operator for real-coded genetic algorithms that is able to avoid the major problems found in this kind of approach such as the premature convergence to local optima, the weakness of genetic algorithms in local fine-tuning and the use of real-coded genetic algorithms instead of the traditional binary-coded problems. Mathematical morphology operations have been employed with this purpose adapting its meaning from other application fields to the generation of better individuals along the evolution in the convergence process. This new crossover technique has been called mathematical morphology crossover (MMX) and it is described along with the resolution of systematic experiments that allow to test its high speed of convergence to the optimal value in the search space.KeywordsGenetic AlgorithmLocal OptimumCrossover OperatorPremature ConvergenceMathematical MorphologyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.