Case-Based Anytime Learning

Integrating Approaches: Empirical Evaluations

We discuss a case-based method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Anytime learning is a general approach to continuous learning in a changing environment. A genetic algorithm with a case-based component provides an appropriate search mechanism for anytime learning. When the genetic algorithm is restarted, strategies which were previously learned under similar environmental conditions are included in the initial population of the genetic algorithm. We have evaluated the system by comparing performance with and without the case-based component, and case-based initialization of the population results in a significantly improved performance.