This paper describes a learning scheme of a structural connectionist architecture based on a simple genetic approach to adaptive processing of tree-structures representation. Conventionally, one of the most popular supervised learning formulations of tree-structures processing is Backpropagation Through Structures (BPTS) . The BPTS algorithm has been successfully applied to a number of learning tasks that involved complex symbolic structural patterns such as image semantic, internet behaviour, and chemical compound. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose a simple genetic evolution approach for this processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified binary chromosome structures. Experimental results significantly support the capabilities of our proposed approach to classify and recognize structural patterns in terms of generalization capability.