Fuzzy motivations for evolutionary behavior learning by a mobile robot
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Bioinformatics integration framework for metabolic pathway data-mining
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Feed-forward artificial neural network based inference system applied in bioinfonnatics data-mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Meta-learning based optimization of metabolic pathway data-mining inference system
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Inference system using softcomputing and mixed data applied in metabolic pathway datamining
International Journal of Data Mining and Bioinformatics
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Fuzzy based models have been used in many areas of research. One issue with these models is that rule bases have the potential for indiscriminant growth. Inference systems with large number of rules can be overspecified, have model comprehension issues and suffer from bad performance. In this research we investigate the use of a genetic algorithm towards the generation of a fuzzy inference system (FIS). We propose using a GA with a dynamic penalty function to manage the rule size of the fuzzy inference system (FIS) while maintaining the exploration of good rules. We apply this method towards the generation of a fuzzy classifier for the search of metabolic pathways. The GA based FIS includes novel mutation and a penalty based fitness scheme which enables the generation of an efficient and compact set of fuzzy rules. Encouraging implementation results are presented for this method as compared with other classification methods. This method should be applicable to a variety of other modelling and classification problems.