Evolutionary programming was originally introduced as a technique
to predict arbitrary time series in light of any specified criteria.
The method operated on finite state machines, these being general
transducers. Since the 1960's, the procedure has been extended
to operate on any chosen representation for a variety of function
optimization and co-evolutionary tasks. This paper provides
a review of the framework of evolutionary programming, identifies
some of its mathematical properties as a search algorithm, and
discusses some potential avenues for improving the performance
of evolutionary programming (and other evolutionary algorithms)
in light of the given problem at hand.
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