Uncertainty analysis of an evolutionary algorithm to develop remote sensing spectral indices

The need for information extracted from remotely sensed data has increased in recent decades. To address this issue, research is being conducted to develop a complete multi-stage supervised object recognition system. The first stage of this system couples genetic programming with standard unsupervised clustering algorithms to search for the optimal preprocessing function. This manuscript addresses the quantification and the characterization of the uncertainty involved in the random creation of the first set of candidate solutions from which the algorithm begins. We used a Monte Carlo type simulation involving 800 independent realizations and then analyzed the distribution of the final results. Two independent convergence approaches were investigated: [1] convergence based solely on genetic operations (standard) and [2] convergence based on genetic operations with subsequent insertion of new genetic material (restarting). Results indicate that the introduction of new genetic material should be incorporated into the preprocessing framework to enhance convergence and to reduce variability.