Abstract
In the paper a method that moderates code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for the simplification of programs. If every individual program in each generation is simplified, then the performance of the genetic programming system is slightly worsened. But if simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained.
Cite
CITATION STYLE
Ekárt, A. (2000). Shorter fitness preserving genetic programs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1829, pp. 73–83). Springer Verlag. https://doi.org/10.1007/10721187_5
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