SEMANTIC APPROXIMATION BASED OPERATOR FOR REDUCING CODE BLOAT IN GENETIC PROGRAMMING
Keywords:Genetic Programming, Semantic Approximation, Code Growth, Code Bloat
In Genetic Programming, code bloat is a well-known problem that is the increase in the average program size without a corresponding improvement in fitness. In order to address this problem, we proposed a new operator called Prune and Plant based on Approximate Terminal (shortened as PP-AT). PP-AT aims at reducing GP code bloat. It replaces a random subtree in a parent by an approximate tree of semantics to obtain the first offspring. This subtree is also added to the population as the second offspring. PP-AT is tested on fifteen regression problems and compared to standard GP and three recent bloat control methods. The experimental results demonstrate that PP-AT outperforms standard GP and other bloat control methods under comparison.