On generalisation of machine learning with neural-evolutionary computations

Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such as network size, initial conditions, learning rate, weight decay factor, pruning algorithms, and many more. In spite of continuous research efforts, we could not arrive at a practical solution which can offer a superior generalisation. We present a novel approach for handling complex problems of machine learning. A multiobjective genetic algorithm is used for identifying (near-) optimal subspaces for hierarchical learning. This strategy of explicitly partitioning the data for subsequent mapping onto a hierarchical classifier is found both to reduce the learning complexity and the classification time. The classification performance of various algorithms is compared and it is argued that the neural modules are superior for learning the localised decision surfaces of such partitions and offer better generalisation.