Evolutionary design and applications of hybrid intelligent systems

摘要

We describe in this paper the evolutionary design of hybrid intelligent systems using Hierarchical Genetic Algorithms (HGAs). The evolutionary approach can be used for fuzzy system optimisation in intelligent control. In particular, we consider the problem of optimising the number of rules and membership functions using an evolutionary approach. The HGA enables the optimisation of the fuzzy system design for a particular application. We illustrate the approach with two cases of intelligent control. Simulation results for both applications show that we are able to find an optimal set of rules and membership functions for the fuzzy control system. We also describe the application of the evolutionary approach for the problem of designing hybrid intelligent systems in time series prediction. In this case, the goal is to design the best predictor for complex time series. Simulation results show that the evolutionary approach optimises the hybrid intelligent systems in time series prediction.