Abstract
Program bloat is a fundamental problem in the field of
Genetic Programming (GP). Exponential growth of
redundant and functionally useless sections of programs
can quickly overcome a GP system, exhausting system
resources and causing premature termination of the
system before an acceptable solution can be found.
Simplification is an attempt to remove such
redundancies from programs. This paper looks at the
effects of applying an algebraic simplification
algorithm to programs during the GP evolution. The GP
system with the simplification is examined and compared
to a standard GP system on four regression and
classification problems of varying difficulty. The
results suggest that the GP system employing a
simplification component can achieve superior
efficiency and effectiveness to the standard system on
these problems.
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