GPFIS-Control: A Genetic Fuzzy System For Control Tasks

Abstract This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFIS-Control). It is based on Multi-Gene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFIS-Control are considered: the Cart-Centering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFIS-Control in relation to other GFCs found in the literature.

[1]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.

[3]  Kevin D. Seppi,et al.  Exposing origin-seeking bias in PSO , 2005, GECCO '05.

[4]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[5]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[6]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[7]  A. J. Yuste,et al.  Knowledge Acquisition in Fuzzy-Rule-Based Systems With Particle-Swarm Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[8]  X. Li,et al.  A New Adaptive Particle Swarm Optimization Algorithm , 2008, 2008 International Workshop on Modelling, Simulation and Optimization.

[9]  Edward Tunstel,et al.  On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control , 1996, Intell. Autom. Soft Comput..

[10]  Gisbert Schneider,et al.  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training , 2006, BMC Bioinformatics.

[11]  Bin-Da Liu,et al.  Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[12]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[13]  Bo Yang,et al.  A Modified Particle Swarm Optimization Algorithm with Dynamic Adaptive , 2007, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007).

[14]  Keiichiro Yasuda,et al.  A Basic Study of the Adaptive Particle Swarm Optimization , 2005 .

[15]  Dominic P. Searson,et al.  Co‐evolution of non‐linear PLS model components , 2007 .

[16]  Oscar Castillo,et al.  A review on the design and optimization of interval type-2 fuzzy controllers , 2012, Appl. Soft Comput..

[17]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[18]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Radu-Emil Precup,et al.  A survey on industrial applications of fuzzy control , 2011, Comput. Ind..

[20]  Sean Luke,et al.  Lexicographic Parsimony Pressure , 2002, GECCO.

[21]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[22]  Keiichiro Yasuda,et al.  Particle Swarm Optimization with Parameter Self-Adjusting Mechanism , 2010 .

[23]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[24]  Thomas Stützle,et al.  Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence , 2008 .

[25]  Keiichiro Yasuda,et al.  Adaptive Particle Swarm Optimization; Self-coordinating Mechanism with Updating Information , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[26]  George J. Klir,et al.  Fuzzy sets and fuzzy logic , 1995 .

[27]  Francisco Herrera,et al.  A learning process for fuzzy control rules using genetic algorithms , 1998, Fuzzy Sets Syst..

[28]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[29]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[30]  Ricardo Tanscheit,et al.  Hierarchical Type-2 Neuro-Fuzzy BSP Model , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[31]  Keiichiro Yasuda,et al.  Adaptive particle swarm optimization using information about global best , 2007 .

[32]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[33]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[34]  Omer Deperlioglu,et al.  Adaptive fuzzy logic controller for DC-DC converters , 2009, Expert Syst. Appl..

[35]  Athanasios Tsakonas,et al.  Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming , 2013, Expert Syst. Appl..

[36]  Simone A. Ludwig Towards a repulsive and adaptive particle swarm optimization algorithm , 2013, GECCO '13 Companion.

[37]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[38]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[39]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[40]  Janusz Kacprzyk,et al.  Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice , 2011, Studies in Fuzziness and Soft Computing.

[41]  Xiufen Li,et al.  A Self-Adaptive Particle Swarm Optimization Algorithm , 2008, 2008 International Conference on Computer Science and Software Engineering.

[42]  Albert A. Groenwold,et al.  A Study of Global Optimization Using Particle Swarms , 2005, J. Glob. Optim..

[43]  Oscar Cordón,et al.  International Journal of Approximate Reasoning a Historical Review of Evolutionary Learning Methods for Mamdani-type Fuzzy Rule-based Systems: Designing Interpretable Genetic Fuzzy Systems , 2022 .

[44]  Nikhil R. Pal,et al.  SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers , 2003, IEEE Trans. Evol. Comput..

[45]  Antonello Rizzi,et al.  Genetic optimization of a fuzzy control system for energy flow management in micro-grids , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[46]  Enrique Alba,et al.  Type-constrained genetic programming for rule-base definition in fuzzy logic controllers , 1996 .

[47]  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.

[48]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .

[49]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[50]  Zhihua Cui,et al.  Self-learning Particle Swarm Optimization Based on Environmental Feedback , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[51]  Mahmoud Moghavvemi,et al.  Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network , 2013 .

[52]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[53]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..