Abstract
This paper is concerned with the generalisation performance of GP. We examine the generalisation of GP on some well-studied test problems and also critically examine the performance of some well known GP improvements from a generalisation perspective. From this, the need for GP practitioners to provide more accurate reports on the generalisation performance of their systems on problems studied is highlighted. Based on the results achieved, it is shown that improvements in training performance thanks to GP-enhancements represent only half of the battle.
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Costelloe, D., Ryan, C. (2009). On Improving Generalisation in Genetic Programming. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_6
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DOI: https://doi.org/10.1007/978-3-642-01181-8_6
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