Using Genetic Search to Refine Knowledge-based Neural Networks

An ideal inductive-learning algorithm should exploit all available resources, such as computing power and domain-specific knowledge, to improve its ability to generalize. Con-nectionist theory-refinement systems have proven to be effective at utilizing domain-specific knowledge; however, most are unable to exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm, which uses genetic algorithms to broaden the type of networks seen during its search. It does this by using (a) the domain theory to help create an initial population and (b) crossover and mutation operators specifically designed for knowledge-based networks. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.