Zusammenfassung
One of the most important and challenging areas of
research in evolutionary algorithms is the
investigation of ways to successfully apply
evolutionary algorithms to larger and more complicated
problems. In this paper, we apply GGP (Generic Genetic
Programming) to evolve general recursive functions for
the even-n-parity problem from noisy training examples.
GGP is very flexible and programs in various
programming languages can be acquired. Moreover, it is
powerful enough to handle context-sensitive information
and domain-dependent knowledge. A number of experiments
have been performed to determine the impact of noise in
training examples on the speed of learning.
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