Size Control Via Size Fair Genetic Operators In The PushGP Genetic Programming System

The growth of program size during evolution (code "bloat") is a well-documented and well-studied problem in genetic programming. This paper examines the use of "size fair" genetic operators to combat code bloat in the PushGP genetic programming system. Size fair operators are compared to naive operators and to operators that use "node selection" as described by Koza. The effects of the operator choices are assessed in runs on symbolic regression, parity and multiplexor problems (2,700 runs in total). The results show that the size fair operators control bloat well while producing unusually parsimonious solutions. The computational effort required to find a solution using size fair operators is about equal to, or slightly better than, the effort required using the comparison operators.

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