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Nowadays, neural networks (NNs) are widely applied in the solution of several real world problems. They have been successfully used in many fields such as chemistry, physics, engineering, and bio-informatics among others. However, their use often relies on some handcrafted settings, such as the number of layers and neurons. This chapter will discuss the Structural Risk Minimization (SRM) problem using some multiobjective optimization concepts. Both are closely related to the classical Tikhonov’s regularization scheme, and, it is also exploited in this work. A neural network is a learning machine capable to describe, to the input x, the set of functions

Recent Advances in Neural Networks Structural Risk Minimization Based on Multiobjective Complexity Control Algorithms