Inductive Operators and Rule Repair in a Hybrid Genetic Learning System: Some Initial Results

Symbolic knowledge representation schemes have been suggested as one way to improve the performance of classifier systems in the context of complex, real-world problems. The main reason for this is that unlike the traditional binary string representation, high-level languages facilitate the exploitation of problem specific knowledge. However, the two principal genetic operators, crossover and mutation, are, in their basic form, ineffective with regard to discovering useful rules in such representations. Moreover, the operators do not take into account any environmental cues which may benefit the rule discovery process. A further source of inefficiency in classifier systems concerns their capacity for forgetting valuable experience by deleting previously useful rules.