Abstract
This paper presents a study of fitness distance correlation and negative slope coefficient as measures of problem hardness for genetic programming. Advantages and drawbacks of both these measures are presented both from a theoretical and empirical point of view. Experiments have been performed on a set of well-known hand-tailored problems and “real-life-like” GP benchmarks.
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Vanneschi, L., Tomassini, M., Collard, P., Clergue, M. (2005). A Survey of Problem Difficulty in Genetic Programming. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_7
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DOI: https://doi.org/10.1007/11558590_7
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