Automatic generation of binary feature detectors

The authors discuss the automatic generation of feature detectors, which is the major task in the design of classical pattern recognition systems. They present a software environment that, in place of human intuition, utilizes learning strategies and stochastic search procedures to guide the generation process. The environment allows the exploration of evolutionary learning processes and adaptive control mechanisms. A preliminary experiment with a two-class recognition system is described, and initial observations are discussed. The recognition task requires the classification of upper case English letters into two categories: target and nontarget.<<ETX>>