Applications of hybrid learning to automated system design

The evolution of biological systems demonstrates the potential inherent in nonstructured performance-drive design processes for solving difficult design problems. A hybrid learning testbed is described that uses adaptive learning techniques which differ from conventional highly structured AI techniques and instead emulate nature´s methods. The testbed incorporates genetic learning, neural networks, and clustering algorithms. The use of these techniques as a means of automating the design of pattern recognition systems is explored. The testbed provides a tangible focus for studying the key components of automated design: model representations, search strategies, and evaluation criteria. It demonstrates how a variety of adaptive techniques can be applied to the automated design of pattern recognition systems