Evolutionary Search for Interesting Behavior of Neural Network Ensembles

Very useful outcome of a neural network model is that relationship of input and output variables can be plotted revealing some potentially interesting information about a modeled system. However this approach is not often used because there are several problems appearing from a closer look. At first there is a problem with the "curse of dimensionality", secondly the problem of model credibility arises when system state space is not fully covered by training data. There are also problems with irrelevant input variables, with the time needed to find some useful plot in multidimensional state space, etc. This paper shows that all these problems can be successfully overcome using modern techniques of evolutionary computation and ensemble modeling. The result of our research is an application that is able to automatically locate interesting plots of system behavior.