Abstract
We propose a multi-objective evolutionary algorithm to generate a set of fuzzy rule-based systems with different trade-offs between accuracy and complexity. The novelty of our approach resides in performing concurrently learning of rules and learning of the membership functions which define the meanings of the labels used in the rules. To this aim, we represent membership functions by the linguistic 2-tuple scheme, which allows the symbolic translation of a label by considering only one parameter, and adopt an appropriate two-variable chromosome coding. Results achieved by using a modified version of PAES on a real problem confirm the effectiveness of our approach in increasing the accuracy and decreasing the complexity of the solutions in the approximated Pareto front with respect to the single objective-based approach.
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References
Alcalá, R., Alcalá-Fdez, J., Herrera, F.: A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE T. Fuzzy Systems 15(4), 616–635 (2007)
Alcalá, R., Gacto, M.J., Herrera, F., Alcalá-Fdez, J.: A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 15(5), 539–557 (2007)
Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds.): Accuracy improvements in linguistic fuzzy modelling. Studies in Fuzziness and Soft Computing, vol. 129. Springer, Heidelberg (2003)
Cococcioni, M., Ducange, P., Lazzerini, B., Marcelloni, F.: A Pareto-based multi-objective evolutionary approach to the identification of mamdani fuzzy systems. Soft Computing 11, 1013–1031 (2007)
Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)
Cordón, O., Herrera, F., Sánchez, L.: Solving electrical distribution problems using hybrid evolutionary data analysis techniques. Applied Intelligence 10, 5–24 (1999)
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, pp. 187–202 (1993)
Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE T. Fuzzy Systems 8(6), 746–752 (2000)
Ishibuchi, H., Yamamoto, T.: Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems 141(1), 59–88 (2004)
Ishibuchi, H., Nojima, Y.: Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int. J. of Approximate Reasoning 44(1), 4–31 (2007)
Knowles, J.D., Corne, D.W.: Approximating the non dominated front using the Pareto archived evolution strategy. Evolutionary Computation 8(2), 149–172 (2000)
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Ducange, P., Alcalá, R., Herrera, F., Lazzerini, B., Marcelloni, F. (2008). Knowledge Base Learning of Linguistic Fuzzy Rule-Based Systems in a Multi-objective Evolutionary Framework. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_92
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DOI: https://doi.org/10.1007/978-3-540-87656-4_92
Publisher Name: Springer, Berlin, Heidelberg
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