Abstract
An important problem with genetic programming systems is that in the course of evolution the size of individuals is continuously growing without a corresponding increase in fitness. This paper reports the application of a self-improvement operator in combination with a characteristic based selection strategy to a classical genetic programming system in order to reduce the effects of code growth. Two examples, a symbolic regression problem and an 11-bit multiplexer problem are used to test and validate the performance of this newly designed operator. Instead of simply editing out non-functional code this method tries to select subtrees with better fitness. Results show that for both test cases code growth is substantially reduced obtaining a reduction factor of 3–10 (depending on the problem) while the same level of fitness is attained.
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Wyns, B., Sette, S., Boullart, L. (2004). Self-Improvement to Control Code Growth in Genetic Programming. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2003. Lecture Notes in Computer Science, vol 2936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24621-3_21
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DOI: https://doi.org/10.1007/978-3-540-24621-3_21
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