Hybrid Genetic-Fuzzy Algorithm for Variable Selection in Spectroscopy - de Lima, Telma Woerle; da Silva Soares, Anderson; Coelho, Clarimar José; Salvini, Rogério Lopes; Laureano, Gustavo Teodoro (Artificial Intelligence and Soft Computing, 2013)
This paper presents a hybrid multi-objective genetic fuzzy algorithm for the variable-selection problem in spectroscopy. The problem formulation considers three fitness functions related to linear equations system stability. These fitness functions are models with fuzzy sets that evaluate the fitness solution for pick out the best to crossover. The population diversity is obtained applying the crowding distance method. The study shows that the selection by a fuzzy decision has better results than the selection by non-domination in problems where the fitness weighing is more proper than no-domination solutions.