An empirical study about the behavior of a genetic learning algorithm on searching spaces pruned by a completeness condition

The main difficulty faced by a learning algorithm is to find the appropriate knowledge inside of the huge search space of possible solutions. Typically, the researchers try to solve this problem developing more efficient search algorithms, defining “ad-hoc” heuristic for the specific problem or reducing the expressiveness of the knowledge representation. This work explores an alternative way that consists of reducing the search space using a completeness condition. The proposed model is implemented on NSLV, a fuzzy rule learning algorithm based on genetic algorithms. We present an experimental study of the behavior of NSLV on pruned search spaces. The experimental results show that when we work with these spaces it is possible to find a good trace-off among prediction capacity, complexity of the knowledge obtained and learning time.