SOME EXPERIMENTS IN MACHINE LEARNING USING
VECTOR EVALUATED GENETIC ALGORITHMS

JAMES DAVID SCHAFFER

Dissertation under the direction of Dr. John R. Bourne

This dissertation describes experiments conducted to
explore the efficacy of using vector-valued feedback with a
class of adaptive procedures called genetic algorithms. The
software system developed was called VEGA for Vector
Evaluated Genetic Algorithm and was first used on multiple
objective optimization problems. The principle conclusion of
these experiments was that VEGA provided a powerful and
robust search technique for complex multiobjective
optimization problems of high order when little or no a 
priori knowledge was available to guide the search. These
results were similar to those found by previous researchers
using scalar genetic algorithms for scalar optimization
problems.
The VEGA technique was then applied to multiclass 
pattern discrimination tasks. The resulting software system
was called LS-2 for Learning System - Two since it followed 
closely the lead of a scalar-valued learning system called 
LS-1 developed by Stephen Smith. The experiments revealed
that LS-2 was able to evolve high performance production
system programs to perform the pattern discrimination tasks
it was given. In addition, experiments which varied several
of the parameters of LS-2 revealed something of the
sensitivity of vector-valued genetic search to the settings 
of these parameters.
In sum it may be said that the VEGA approach has
demonstrated the efficacy of extending the previously
demonstrated power of the genetic algorithms to vector-valued
problems and thereby provides a new approach to machine 
learning.