Improved learning in genetic rule-based classifier systems

Many learning algorithms tend to converge into local minima that often represent partial solutions. Schemes are presented that greatly minimize the risk of converging to a partial solution and maximize the rule discovery process for rule-based learning. For the experiments, a generic algorithm rule-based learning system called a classifier system has been used. The new strategies are supported by presenting accelerations and completion of learning in higher order letter image classification problems.<>