Hybrid self-configuring evolutionary algorithm for automated design of fuzzy logic rule base

In this paper a method for fuzzy logic systems design, which implements the latest developments in this field, is presented. The main evolutionary algorithm uses the Pittsburgtype approach, and the Michigan-type one is used as a mutation operator. A self-configuring technique is used to adjust the algorithm parameters based on their success rates. The novelty here is the algorithm's ability to adjust the probability using either the genetic or heuristic method for the incorporation of a new rule in the rule base. Previously, this was done voluntarily. It is demonstrated that this new algorithm's flexibility does not decrease its performance although it makes it fully automated.

[1]  Hisao Ishibuchi,et al.  Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.

[2]  Eugene Semenkin,et al.  Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator , 2012, ICSI.

[3]  H. Ishibuchi.  Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .

[4]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[5]  Eugene Semenkin,et al.  Spacecrafts' Control Systems Effective Variants Choice with Self-configuring Genetic Algorithm , 2012, ICINCO.

[6]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[7]  Eugene Semenkin,et al.  Stochastic Models and Optimization Algorithms for Decision Support in Spacecraft Control Systems Preliminary Design , 2014 .

[8]  Beatrice Lazzerini,et al.  Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets , 2010, Soft Comput..

[9]  Jesús Alcalá-Fdez,et al.  Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation , 2007, Int. J. Approx. Reason..

[10]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[11]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[12]  Francisco Herrera,et al.  Ten Lectures on Genetic Fuzzy Systems , 1997 .

[13]  H. Ishibuchi,et al.  Multi-objective genetic local search for minimizing the number of fuzzy rules for pattern classification problems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[14]  Francisco Herrera,et al.  A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.

[15]  Hisao Ishibuchi,et al.  Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation , 2013, IEEE Transactions on Fuzzy Systems.