Rule Discovery Technique Using GP with Crossover to Maintain Variety

Many GP learning methods have been proposed to decrease node combinations in order to keep the node combinations from explosively increasing. We propose a technique using an opposite approach which tests a greater number of combinations in order to decrease the chances of the search being 'trapped' in a local optimum. In the proposed technique, how 'different' the individual structure is is used as an index in selecting individuals for genetic operations. Therefore, variety in the GP group is strongly maintained, and it is expected that GP learning is always done to a new combination.