Genetic evolution processing of data structures for image classification Siu-Yeung Cho and Zheru Chi   View Paper (PDF)   Return to February 2005, Volume 17, Issue 2 Abstract . The BPTS algorithm has been successfully applied to a number of learning tasks that involved structural patterns such as image, shape, and texture classifications. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose the genetic evolution for this data structures processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified chromosome structures. Also, the fitness evaluation as well as the adaptive crossover and mutation for this structural genetic processing are investigated in this paper. An application to flowers image classification by a structural representation is provided for the validation of our method. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness. ©2006 Association for Computing Machinery