Real-valued genetic algorithms for fuzzy grey prediction system

A genetic-based fuzzy grey prediction model is proposed in this paper. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-valued handled by the presented algorithms. To prevent the system from turning into a premature problem, we select the elitists from two groups of possible solutions to reproduce the new populations. To verify the effectiveness of the proposed genetic algorithms, two simple functions are first tested. The results show that our method outperforms the conventional one no matter whether from the viewpoint of the number of iterations required to find the optimum solutions or from the final solutions obtained. The real-valued genetic algorithms are then exploited to optimize the fuzzy controller which is designed to perform the compensation job. Two different types of fuzzy inference rules are considered to compensate for the predicted errors from the grey model. The difficulty encountered in applying the genetic algorithms to adjusting the fuzzy parameters is also discussed. Based on the simulation results from the problems of the weather forecast, we found that the proposed methodology is very effective in determining the quantity of compensation for the predicted outputs from the traditional grey approach. (C) 1997 Elsevier Science B.V.