
Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 46871, 1995
USING EVOLUTIONARY COMPUTATION TO GENERATE TRAINING SET DATA FOR NEURAL NETWORKS?
Dan Ventura
Tim Andersen
Tony R. Martinez
Provo, Utah 84602Computer Science Department, Brigham Young University email: dan@axon.cs.byu.edu, tim@axon.cs.byu, martinez@cs.byu.edu
Most neural networks require a set of training examples in order to attempt to approximate a problem function. For many realworld problems, however, such a set of examples is unavailable. Such a problem involving feedback optimization of a computer network routing system has motivated a general method of generating artificial training sets using evolutionary computation. This paper describes the method and demonstrates its utility by presenting promising results from applying it to an artificial problem similar to a realworld network routing optimization problem.
Introduction
Many inductive learning algorithms based on neural networks, machine learning, and other approaches have been developed and have been shown to perform reasonably well on a variety of problems [2][4]. Typically, neural networks (NN) perform inductive learning through the presentation of preclassified examples; however,
one of the largest obstacles faced in
applying these algorithms to realworld
problems is the lack of such a
set of training examples. Many
times collecting data for a training
set is the most difficult facet of a
problem.
This paper presents such a
realworld problem  one for which
no training data exists and for which
gathering such data is at best
extremely expensive both in time
and in resources. To remedy the
lack of training set data, a method
using evolutionary computation (EC)
[3][8] is described in which the
survivors of the evolution become
the training examples for a neural
network. The synthesis of EC with
NN provides both initial
unsupervised random exploration of
the solution space as well as
supervised generalization on those
initial solutions. Work involving a
combination of EC and NN is
becoming more prevalent; the reader
is referred to [1][5][6][7] for
examples.
? This research was funded in part by a grant from Novell, Inc.