About the project
This research presents the FSALPS (Feature Selection Age Layered Population Structure) evolutionary algorithm. FSALPS performs effective feature subset selection and classification of varied supervised learning tasks. It is a modication of Hornby's ALPS algorithm, which is a renown meta-heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies.
The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal selection for the construction of GP trees/sub-trees. FSALPS continuously refines the feature subset selection process while simultaneously evolving efficient classifiers through a non-converging evolutionary process that favors selection of features with high discrimination of class labels. The research applies FSALPS an assortment of high-dimensional classification datasets, including a hyperspectral image.
Comparative experiments show that ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. Furthermore, FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.
"Feature Selection and Classification Using Age Layered Population Structure Genetic Programming",
A. Awuley and B.J. Ross, (accepted) CEC 2016, Vancouver BC, July 2016.