Genetic Multiobjective Optimisation with Elite Insertion for EEG Feature Selection

Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly re-designing the classifier for subsets of features. Naturally, this problem is formulated as a multi-objective optimization (MOO) addressing to the accuracy of the classifier and the parsimony of the feature vector. In MOOs, common ranking techniques use dominance analysis for providing a partial sorting of the solutions. Unfortunately, dominance analysis can also promote solutions less useful for the application. In order to gradually guide the search towards a user-preferred area set around the middle of the best fronts, this paper proposes an adaptive ranking algorithm with insertion of elites (ARE), which could be integrated in any MOO genetic algorithm. ARE incorporates two new procedures proposed for labeling the preferred solutions and for inserting elites in the less populated areas, whenever a biased exploration is detected. The experimental investigations illustrate that GA with ARE offers better results than NSGAII, both for electroencephalogram (EEG) feature selection problem (which likely involves weakly conflicting objectives) and MOOs with strongly conflicting objectives.