Combining Genetic Algorithms
and Neural Networks
1994, English, 100+ pages,
Master Thesis at the University of Tennessee at Knoxville, USA.
Neural networks and genetic algorithms demonstrate powerful problem
solving ability. They are based on quite simple priciples, but take
advantage of their mathematical nature: non-linear iteration.
Neural Networks with backpropagation learning showed results by
searching various kinds of functions. However, the choice of the basic
parmeter (network topology, learning rate, initial weights) often already
determines the success of the training process. The selection of these
parameter follow in practical use rules of thumb, but their value is
at most arguable.
Genetic algorithms are global search methods that are based on principles
like selection, crossover and mutation. This thesis examines how
genetic algorihtms can be used to opimize the network topology etc. of neural
networks. It investigates, how various encoding strategies influence
the GANN synergy. They are evaluated according to their performance on
academic and practical problems of different complexity.
A research tool has been implemented, using the programming language
C++. Its basic properties are described.