Coevolutionary Construction of Features for Transformation of Representation in Machine Learning

The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.

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