Evolution and adaptation of neural networks

One important issue in developing dynamic algorithms that changes the structure and weights of ANN (artificial neural networks) is how to achieve a proper balance between network complexity and its generalization capability. Typical hybrid approaches to address this problem incorporates EA strategy using a population of backpropagation networks. Since individuals undergo backpropagation networks. Since individuals undergo backpropagation training, this approach is inefficient and inherits the pitfalls of gradient learning. SEPA (structure evolution and parameter adaptation) addresses these issues using an encoding scheme where network weights and connections are encoded in matrices of real numbers. Network parameters are locally encoded and undergo local adaptation with fitness evaluation consisting mainly of fast feed-forward matrix operations that can be implemented in parallel or distributed environment. Experimental results show that SEPA´s strategy produces optimal network structure with fast convergence, high consistency, and good generalization capability.