A study on the performance of solutions generated by  G enetic  P rogramming ( 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.
['Luis E. Da Costa', 'Jacques-André Landry']
Relaxed genetic programming