A Network Genetic Algorithm for Concept Learning

This paper presents a highly parallel genetic algorithm, designed for concept induction in propositional and first order logics. The system exploits niches and species for learning multimodal concepts; it deeply differs from other systems because of the distributed architecture, which totally eliminates the concept of common memory. A first implementation of the system, designed for checking the possibility of exploiting parallel processing in network computer, is evaluated on standard benchmarks. The experimental results show that the system reaches good performances both with respect to the quality of the learned knowledge and with respect to the speed up on a workstation cluster.