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. © Springer-Verlag 2004.
CITATION STYLE
Wyns, B., Sette, S., & Boullart, L. (2004). Self-Improvement to Control Code Growth in Genetic Programming. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2936, 256–266. https://doi.org/10.1007/978-3-540-24621-3_21
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