Coevolution and learning symbolic concepts: statistical validation
Filippo Neri · 2022
The paper reports an empirical study done to statistically validate the preliminary findings obtained in previous research by the author on the small disjunct problem. Thus additional support to the working hypothesis that cooperative evolution (co-evolution) can be successfully applied in learning symbolic concepts and that co-evolution when carefully exploited can produce more robust classification rule (symbolic concepts) with higher statistical validity. In the paper we will compare the effect of applying a specific co-evolutive learning strategy with the results obtained by running a learning system without any coevolution. Thus we can measure the add-on effect produced by the coevolutive strategy. As learning systems we will use the system REGAL that combines distributed learning and genetic algorithms to find symbolic classifiers. As a future extension of this research, we note that the described co-evolutive strategy can be applied to other learning methods.