Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
A Study on Lateral Control of Autonomous Vehicles via Fired Fuzzy Rules Chromosome Encoding Scheme
Journal of Intelligent and Robotic Systems
Evolvability and speed of evolutionary algorithms in light of recent developments in biology
Journal of Artificial Evolution and Applications
A new principal curve algorithm for nonlinear principal component analysis
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Dynamic evolution of the genetic search region through fuzzy coding
Engineering Applications of Artificial Intelligence
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding compared with alternatives, where the parameters are directly represented in the encoding. Fuzzy coding is described and compared with conventional binary coding, gray coding, and floating-point coding. Two test examples, along with neural identification of a nonlinear pH process from experimental data, are studied. It is shown that fuzzy coding is better than the conventional methods and is effective for parameter optimization in problems where the search space is complicated.