Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem

This paper presents an evolutionary synthesis of feature extraction programs for object recognition. The evolutionary synthesis method employed is based on linear genetic programming which is combined with redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem, without any domain-specific knowledge. Experiments were done on a lawn weed detection problem with both a low-level performance measure, i.e., segmentation accuracy, and an application-level performance measure, i.e., simulated weed control performance. Compared with four human-designed lawn weed detection methods, the results show that the performance of synthesized feature extraction programs is significantly better than three human-designed methods when evaluated with the low-level measure, and is better than two human-designed methods according to the application-level measure.