Abstract
The tuning of Fuzzy Rule-Based Systems is often applied to improve their performance as a post-processing stage once an initial set of fuzzy rules has been extracted. This optimization problem can become a hard one when the size of the considered system in terms of the number of variables, rules and, particularly, data samples is big. Distributed Genetic Algorithms are excellent optimization algorithms which exploit the nowadays available parallel hardware (multicore microprocessors and clusters) and could help to alleviate this growth in complexity. In this work, we present a study on the use of the Distributed Genetic Algorithms for the tuning of Fuzzy Rule-Based Systems. To this end, we analyze the application of a specific Gradual Distributed Real-Coded Genetic Algorithm which employs eight subpopulations in a hypercube topology and local parallelization at each subpopulation. We tested our approach on nine real-world datasets of different sizes and with different numbers of variables. The empirical performance in solution quality and computing time is assessed by comparing its results with those from a highly effective sequential tuning algorithm. The results show that the distributed approach achieves better results in terms of quality and execution time as the complexity of the problem grows.