Optimisation with real-coded genetic algorithms based on mathematical morphology

The goal of this work is to propose a novel approach to function optimisation by evolutionary techniques, in particular, real-coded genetic algorithms. A new genetic crossover operator, suitable for real codification, has been designed. This operator is called morphological crossover as it is based on mathematical morphology theory. The morphological crossover includes a new genetic diversity measure that has low computational cost. This operator is presented along with the resolution of a set of optimisation problems, including neural network training. The results are compared to other optimisation approaches as gradient descent methods or binary and real-coded generic algorithms using different crossover operators. These tests show that the properties exhibited by the proposed operator when using real-coded genetic algorithms give higher convergence speed and less probability of being trapped in a local optimum.