Abstract
An unususal GP implementation is proposed, based on a
more "economic" exploitation of the GP algorithm:
the "individual" approach, where each individual of
the population embodies a single function rather than a
set of functions. The final solution is then a set of
individuals. Examples are presented where results are
obtained more rapidly than with the conventional
approach, where all individuals of the final generation
but one are discarded.
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