Abstract
A comparison between four Genetic Programming
techniques is presented in this paper. The compared
methods are Multi-Expression Programming, Gene
Expression Programming, Grammatical Evolution, and
Linear Genetic Programming. The comparison includes all
aspects of the considered evolutionary algorithms:
individual representation, fitness assignment, genetic
operators, and evolutionary scheme. Several numerical
experiments using five benchmarking problems are
carried out. Two test problems are taken from PROBEN1
and contain real-world data. The results reveal that
Multi-Expression Programming has the best overall
behavior for the considered test problems, closely
followed by Linear Genetic Programming.
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