Adaptive scheduling of optimization algorithms in the construction of interpolative fuzzy systems

This paper presents an adaptive scheduling approach applied for constructing interpolative fuzzy rule based systems. This is a continuation of our preceding work, where the same approach was used for dense fuzzy rule bases. During the optimization process different optimization algorithms are scheduled according to their respective local efficiency, i.e. according to their convergence speed in various phases of the machine learning process. The scheduled optimization techniques are evolutionary algorithms that have shown efficiency in the construction of interpolative fuzzy rule based systems. Simulations are carried out on standard benchmark sets in order to evaluate the established system and to compare it to fuzzy systems built up by deploying the same optimization techniques without the scheduling approach.