Case-base injection schemes to case adaptation using genetic algorithms

Abstract

Case adaptation has always been a difficult process to engineer within the case-based reasoning (CBR) cycle. To combat the difficulties of CBR adaptation, such as its domain dependency, computational cost and the inability to produce novel cases to solve new problems, genetic algorithms (GAs) have been applied to CBR adaptation. As the quality of cases stored in a case library has a significant effect on the solutions produced by a case-based reasoner, it is important to investigate the impact of the quality and quantity of cases injected into a GA initial population for adapting fitter solutions to new problems. This work explores a method applying a GA to CBR adaptation, where a learning mechanism is applied to feed knowledge back from the CBR revision stage into the reuse stage, allowing the GA to learn which mutations result in invalid solutions. In collaboration with this learning mechanism, the number of cases to be injected, and the fitness of cases to be injected from retrieval into reuse is explored. The fitness of adapted cases and their response to our developed learning feedback is also trialled through varying the size and quality of the GA initial population. © Springer-Verlag 2004.