Recurrent genetic programming
Ankur M. Teredesai, Venu Govindaraju, Eugene H. Ratzlaff, J. Subrahmonia · 2003
A typical pattern recognition system consists of two stages: the pre-processing stage to extract features from the data, and the classification stage to assign the feature vector a class label. There are two kinds of feature extraction techniques with respect to the kind of data: the fixed number of features per sample generating a fixed length feature vector, and the fixed number of features per subsample generating a variable length feature vector due to variable number of sub-samples (frames) for each input pattern. The first kind is the most commonly used feature vector for classification methods. The second kind is usually extracted in domains where the input sample is time-variant. Traditionally a separate class of machine learning algorithms consisting of hidden Markov models, recurrent neural networks, etc. have been employed for classification of time variant signals. Evolutionary computation techniques like genetic algorithms and genetic programming have also been used previously to optimize the architecture for HMMs or learning the weights for recurrent-neural networks. We describe a recurrent framework for genetic programming (GP). This framework helps place GP in the class of machine learning algorithms alongside recurrent neural networks and hidden Markov models. We describe the application of recurrent genetic programming for the classification of on-line handwritten numerals obtained from tablet-based input.