Abstract
This paper describes an approach to online simplification of evolved programs in genetic programming (GP). Rather than manually simplifying genetic programs after evolution for interpretation purposes only, this approach automatically simplifies programs during evolution. In this approach, algebraic simplification rules, algebraic equivalence and prime techniques are used to simplify genetic programs. The simplification based GP system is examined and compared to a standard GP system on a regression problem and a classification problem. The results suggest that, at certain frequencies or proportions, this system can not only achieve superior performance to the standard system on these problems, but also significantly reduce the sizes of evolved programs.
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Zhang, M., Wong, P., Qian, D. (2006). Online Program Simplification in Genetic Programming. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_75
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DOI: https://doi.org/10.1007/11903697_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
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