The Effects of Problem Representation and Network Representation on Training Results of Artificial Neural Networks
Summary
There are different ways to obtain a good Artificial Neural Network. When
training, the choice of the data set is of importance to the quality of the
resulting network. When evolving a network using Genetic Algorithms, it is
important that the representation of the network does not interfere with
the passing-on of information to next generations. I looked into the effects
of data representation on the quality of the trained networks, and I
investigated one solution proposed by Thierens (1996) to unheuristically
remove redundancies in genotype. I could not verify the results found in
the proposed solution.