Relaxed genetic programming
Luis E. Da Costa, Jacques-André Landry · 2006
A study on the performance of solutions generated by Genetic Programming (GP) when the training set is relaxed (in order to allow for a wider definition of the desired solution) is presented. This performance is assessed through 2 important features of a solution: its generalization error and its bloat, a common problem of GP individuals. We show how a small degree of relaxation improves the generalization error of the best solutions; we also show how the variation of this parameter affects the bloat of the solutions generated.