Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems

There has been a considerable effort in the design of evolutionary systems for the automatic generation of neural networks. Symbiotic Adaptive Neuro Evolution (SANE) is a novel approach that carries co-evolution of neural networks at two levels of neuron and network. The SANE network is likely to face problems when the applied data set has high number of attributes or a high dimensionality. In this paper we build a modular neural network with probabilistic sum integration technique to solve this curse of dimensionality. Each module is a SANE network. The division of the problem involves the breaking up of the problem into sub-problems with different (may be overlapping) attributes. The algorithm was simulated for the Breast Cancer database from UCI machine learning repository. Simulation results show that the algorithm, keeping the dimensionality low, was able to effectively solve the problem.