Abstract
Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work.
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© 1997 Springer-Verlag Berlin Heidelberg
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Clack, C., Yu, T. (1997). Performance enhanced genetic programming. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014803
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DOI: https://doi.org/10.1007/BFb0014803
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