Fitness distance correlation in genetic programming: a constructive counterexample
Leonardo Vanneschi, Marco Tomassini, Philippe Collard, Manuel Clergue · 2003
The fitness distance correlation coefficient has been shown to be a reasonable measure to quantify problem difficulty in genetic algorithms and genetic programming for a wide set of problems. In this paper we present an hand-tailored function for which fitness distance correlation fails to correctly predict problem difficulty in genetic programming. This counterexample proves that fitness distance correlation, although reliable, is not an infallible measure to quantify problem difficulty.