Title :
Three-objective optimization in linguistic function approximation
Author :
Ishibuchi, Hisao ; Nakaskima, T.
Author_Institution :
Dept. of Ind. Eng., Osaka Prefectural Univ., Sakai, Japan
Abstract :
Our task is to approximately represent a nonlinear function using a small number of simple linguistic rules such as -If x1 is small and x2 is large then y is large”. Linguistic rules are extracted from numerical input-output data by a multi-objective fuzzy GBML, (genetics-based machine learning) algorithm. In this paper, we first formulate our rule extraction task as a three-objective combinatorial optimization problem. Three objectives are to minimize the total squared error, the number of linguistic rules, and their total length. Then we show how a fuzzy GBML algorithm can be implemented in the framework of multi-objective optimization. This algorithm does not try to find a single rule set but a number of non-dominated rule sets with respect to the three objectives. Finally we illustrate our approach to linguistic modeling through computer simulations on numerical examples
Keywords :
function approximation; fuzzy logic; genetic algorithms; learning (artificial intelligence); combinatorial optimization; computer simulations; genetics-based machine learning algorithm; linguistic function approximation; linguistic modeling; linguistic rules; multi-objective fuzzy GBML; multi-objective optimization; nonlinear function; numerical examples; rule extraction; three-objective optimization; total squared error; Computer simulation; Data mining; Function approximation; Fuzzy neural networks; Fuzzy systems; Industrial engineering; Knowledge based systems; Machine learning; Merging; Training data;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934410
