Representational issues in machine learning
Gunar E. Liepins, Michael R. Hilliard · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1986
Classifier systems are numeric machine learning systems. They are machine counterparts to the natural genetic process and learn by reproduction, crossover, and mutation. Much publicity has been attended to their ability to demonstrate significant learning from a random start and without human intervention. Less well publicized is the considerable care that must be given to the choices of parameter settings and representation. Without the proper ''nurturing environment'' genetic algorithms are apt to learn very little. This infusion of human intelligence is often discounted, but the choice of appropriate representation forms the core of much of the current genetic algorithm research. This paper will address some of the representational issues from the perspective of two current experiments, one with scheduling and the other with a simulated robot. 10 refs., 7 figs.