Abstract
The PSO-PARSIMONY methodology (a heuristic for finding accurate and low-complexity models with particle swarm optimization (PSO)) allows obtaining machine learning models with a good balance between accuracy and complexity. However, when the datasets are of high dimensionality, the methodology does not sufficiently reduce the complexity of the models. This paper presents a new hybrid methodology, called HYB-PARSIMONY, that combines PSO with genetic algorithm (GA) based methods. In the early stages of the optimization process, GA methods have a preponderance to accelerate the search for parsimony. Later, PSO becomes more relevant to improve accuracy. This new methodology obtains significant improvements in the search for more accurate and low-complexity models in high-dimensional datasets.