The objective of this study is to develop a genetic programming (GP) based model to predict constant amplitude fatigue crack propagation life of 2024 T3 aluminum alloys under load ratio effect based on experimental data and to compare the results with earlier proposed ANN model. It is proved that genetic programming can effectively interpret fatigue crack growth rate data and can efficiently model fatigue life of the material system under investigation in comparison to ANN model. crack length (mm) measured from the edge of the plate crack length corresponding to the ‘ith’ step/initial crack length final crack length (mm) crack length corresponding to the ‘(i + 1)th’ step (mm) specimen thickness crack growth rate (mm/cycle) remotely applied load (N) geometrical factor stress intensity factor (MPa√m) maximum stress intensity factor (MPa√m) stress intensity factor range (MPa√m) specific growth rate number of cycles corresponding to the ‘ith’ step number of cycles corresponding to the ‘(i +1)th’ step final (ANN) number of cycles final (GP) number of cycles final (experimental) number of cycles prediction ratio of ANN model prediction ratio of GP model load ratio plate width (mm) thickness (mm) 
Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP