Since pioneering works by Prof. Hisao Ishibuchi in middle nineties, Pareto-based Evolutionary Multiobjective Optimization (EMO) of Fuzzy Rule-Based Systems (FRBSs) is nowadays a well-established research area. It is a branch of the more general Evolutionary/Genetic Fuzzy Systems (see F. Herrera, "Genetic Fuzzy systems: Taxonomy, current research trends and prospects", Evo. Intel. (2008), 1:27-46 and this bibliography page on recent publications on the topic, maintained by R. Alcalá and M. J. Gacto). In Pareto-based evolutionary optimization the set of objetives used are not aggregated in order to reconduct the problem to a single objective optimization problem. This page is intended to collect as many references as possible to papers dealing with Pareto-based EMO of FRBSs. (Pareto-based) EMOs of FRBSs are special cases of Multiobjective Evolutionary Fuzzy Systems (MEFSs), which include the class of Multiobjective Genetic Fuzzy Systems (MGFSs). For a review on the last topic, see H. Ishibuchi, "Multiobjective Genetic Fuzzy Systems: review and future research directions", in Proc. of Fuzz-IEEE'07, pp. 1-6). For a more general overview of multiobjective optimization in machine learning please refer to Y. Jin and B. Sendhoff, "Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies", IEEE Trans. on Syst., Man and Cyb., part C, (2008), 38(3):397- 415. For a more general bibliography on EMO, please refer to the EMOO bibliography page, mantained by Prof. Carlos A. Coello Coello.
The Evolutionary Multiobjective Optimization of Fuzzy Rule-Based Systems Bibliography Page
COCOCCIONI, MARCO
2009-01-01
Abstract
Since pioneering works by Prof. Hisao Ishibuchi in middle nineties, Pareto-based Evolutionary Multiobjective Optimization (EMO) of Fuzzy Rule-Based Systems (FRBSs) is nowadays a well-established research area. It is a branch of the more general Evolutionary/Genetic Fuzzy Systems (see F. Herrera, "Genetic Fuzzy systems: Taxonomy, current research trends and prospects", Evo. Intel. (2008), 1:27-46 and this bibliography page on recent publications on the topic, maintained by R. Alcalá and M. J. Gacto). In Pareto-based evolutionary optimization the set of objetives used are not aggregated in order to reconduct the problem to a single objective optimization problem. This page is intended to collect as many references as possible to papers dealing with Pareto-based EMO of FRBSs. (Pareto-based) EMOs of FRBSs are special cases of Multiobjective Evolutionary Fuzzy Systems (MEFSs), which include the class of Multiobjective Genetic Fuzzy Systems (MGFSs). For a review on the last topic, see H. Ishibuchi, "Multiobjective Genetic Fuzzy Systems: review and future research directions", in Proc. of Fuzz-IEEE'07, pp. 1-6). For a more general overview of multiobjective optimization in machine learning please refer to Y. Jin and B. Sendhoff, "Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies", IEEE Trans. on Syst., Man and Cyb., part C, (2008), 38(3):397- 415. For a more general bibliography on EMO, please refer to the EMOO bibliography page, mantained by Prof. Carlos A. Coello Coello.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
