Evolutionary optimization of growing neural gas parameters for object categorization and recognition

The already introduced Neural Map provides a structural association for the building blocks of dynamically generated object models. Its learning and recall procedures are built upon the Growing Neural Gas algorithm, which is highly parameterized. The values of these parameters are obtained through a time-consuming empirical approach. In the present work, we evaluate the use of optimization based on Evolutionary Algorithms to simplify this task. This paradigm delves into six different approaches given by the combination of three fitness functions and two starting conditions. The performance of the proposed optimization paradigm is cross-validated with experiments on invariant object categorization and recognition found in literature. The results show that the empirically set parameter values can be successfully optimized using this paradigm.