Designing neural network explanation facilities using genetic algorithms
R.C. Eberhart, Roy W. Dobbins · 1991
The authors describe the use of genetic algorithms to provide components of explanation facilities for neural network applications. The genetic algorithm implementation, Genesis, uses a trained backpropagation neural network weight matrix as the genetic algorithm fitness function. Using different combinations of Genesis' run-time options, codebook vectors and decision surfaces are defined for the trained neural network. These vectors and surfaces can then be used as components of a facility that explains how the network is trained, and how it differentiates between classes. Two examples of this methodology are presented and briefly discussed. The first is a network trained to solve the XOR problem. The second is a network trained to diagnose appendicitis.>