A learning process based on covariance matrix adaptation for morphological-linear perceptrons

The dilation-erosion-linear perceptron (DELP) is a morphological-linear model based on fundamentals of mathematical morphology (MM). Its design is a gradient-based learning process using ideas from the backpropagation (BP) algorithm. However, a drawback arises from the gradient estimation of morphological operators, because they are not differentiable of usual way. In this sense, this paper presents an evolutionary learning process, using the covariance matrix adaptation evolutionary strategy (CMAES), to design the DELP model. Furthermore, we conduct an experimental analysis using a relevant set of binary classification problems, and the obtained results are discussed and compared to results found using the DELP model with its classical learning process.