Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)

According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. This paper describes the results of work performed on data gathered by the GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number of false positives and negatives determined.