Evolutionary design and applications of hybrid intelligent systems Online publication date: Wed, 25-Apr-2007
by Oscar Castillo, Patricia Melin
International Journal of Innovative Computing and Applications (IJICA), Vol. 1, No. 1, 2007
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook