Yi Ling Liu
ABSTRACT
This paper suggests a genetic participatory learning algorithm and illustates its use in fuzzy systems modeling. The algorithm emerges from the concepts of participatory learning, selective transfer, and differential evolution. In genetic participatory learning the current population plays an important role in shaping evolution of the population individuals themselves. Selection uses compatibility between best and ramdonly chosen individuals. Exchange of information between individuals employes a recombination operator built from a selective transfer mechanism, whereas mutation proceeds analogously as in differential evolution. Recombination and mutation operations are affected by compatibility between individuals. An application example regarding fuzzy modeling of an electric maintenance problem using actual data serves to illustrate the effectveness of the algorithm, and to compare with alternative participatory and genetic fuzzy systems approaches. Computational results suggest that genetic participatory learning produces accurate and competitive models when compared with current state of the art approaches.
- A. Eiben and J. Smith, Introduction to Evolutionary Computing, Springer, Berlin Heidelberg, 2010. Google Scholar
Digital Library
- F. Rothlauf, Design of Modern Heuristics: Principles and Applications, Springer, Berlin Heidelberg, 2011. Google ScholarDigital Library
- R. Yager, "Participatory Genetic Algorithms," BISC Group List, message posted on August 29, 2000.Google Scholar
- Y. L. Liu and F. Gomide, "Evolutionary Participatory Learning in Fuzzy System Modeling," Proc. IEEE Fuzzy Systems, India, submitted for publication, 2013.Google Scholar
- R. Alcalá, M. Gacto, and F. Herrera, "A Fast and Scalable Multiobjective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems," IEEE Transactions on Fuzzy Systems, vol. 19, no. 4, pp. 666--681, 2011. Google ScholarDigital Library
- Y. L. Liu and F. Gomide, "Fuzzy System Modeling with Participatory Evolution," Proc. Joint IFSA/NAFIPS Conference, Canada, submitted for publication, 2013.Google Scholar
- R. Yager, "A model of participatory learning," IEEE Trans. Syst., Man, Cybern., vol. 20, no. 5, pp. 1229--1234, 1990.Google ScholarCross Ref
- C. Birchenhall, N. Kastrinos, and S. Metcalfe, "Genetic algorithms in evolutionary modelling," J. Evol. Econ., vol. 7, pp. 375--393, 1997.Google ScholarCross Ref
- R. Storn and K. Price, "Differential Evolution - A simple and efficient heuristic for global optimization over continuous spaces," J. Global Optimization, vol. 11, pp. 341--359, 1997. Google ScholarDigital Library
- L. Wang and J. Mendel, "Generation fuzzy rules by learning from examples," IEEE Trans. Syst., Man, Cybern., vol. 22, no. 6, pp. 1414--1427, 1992.Google ScholarCross Ref
Index Terms
- Genetic participatory algorithm and system modeling
Recommendations
An improved class of real-coded Genetic Algorithms for numerical optimization
Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of ...
Population size reduction for the differential evolution algorithm
This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually ...
A computationally efficient evolutionary algorithm for real-parameter optimization
Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on ...
Comments