Abstract
Competitive fitness functions can generate performance
superior to absolute fitness functions Angeline and
Pollack 1993, Hillis 1992. This chapter describes a
method by which competition can be implemented when
training over a fixed (static) set of examples. Since
new training cases cannot be generated by mutation or
crossover, the probabilistic frequencies by which
individual training cases are selected competitively
adapt. We evolve decision trees for the problem of word
sense disambiguation. The decision trees contain
embedded bit strings; bit string crossover is
intermingled with subtree-swapping. To approach the
problem of overlearning, we have implemented a fitness
penalty function specialized for decision trees which
is dependent on the partition of the set of training
cases implied by a decision tree.
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