Abstract
In conventional Genetic Programming (GP), n programs are simultaneously evaluated and only the best programs will survive from one generation to the next. It is a pity as some programs might contain useful code that might be hidden or not evaluated due to the presence of introns. For example in regression, 0× (perfect code) will unfortunately not be assigned a good fitness and this program might be discarded due to the evolutionary process. In this paper, we develop a new form of GP called PhenoGP (PGP). PGP individuals consist of ordered lists of programs to be executed in which the ultimate goal is to find the best order from simple building-blocks programs. If the fitness remains stalled during the run, new building-blocks programs are generated. PGP seems to compare fairly well with canonical GP.
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Fonlupt, C., Robilliard, D. (2013). PhenoGP: Combining Programs to Avoid Code Disruption. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_5
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DOI: https://doi.org/10.1007/978-3-642-37207-0_5
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