Displaying similar documents to “A multicriteria genetic tuning for fuzzy logic controllers.”

Multi-stage genetic fuzzy systems based on the iterative rule learning approach.

Antonio González, Francisco Herrera (1997)

Mathware and Soft Computing

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Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning...

Using genetic feature selection for optimizing user profiles.

Henrik Legind Larsen, Nicolás Marín, María José Martín-Bautista, M. Amparo Vila (2000)

Mathware and Soft Computing

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Most of the techniques used in text classification are determined by the occurrences of the words (terms) appearing in the documents, combined with the user feedback over the documents retrieved. However, in our model, the most relevant terms will be selected from a previous fuzzy classification given by the genetic algorithm guided by the user feedback, but using techniques from Machine Learning. A feature selection process is carried out through a Genetic Algorithm in order to find...

Improvement to the cooperative rules methodology by using the ant colony system algorithm.

Rafael Alcalá, Jorge Casillas, Oscar Cordón, Francisco Herrera (2001)

Mathware and Soft Computing

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The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems. Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one....

A niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction.

Luciano Sánchez Ramos, José Antonio Corrales González (2000)

Mathware and Soft Computing

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A new method for applying grammar based Genetic Programming to learn fuzzy rule based classifiers from examples is proposed. It will produce linguistically understandable, rule based definitions in which not all features are sent in the antecedents. A feature selection is implicit in the algorithm. Since both surface and deep structure will be learned, standard grammar based GP is not applicable to this problem. We have adapted GA-P algorithms, a method formerly defined as an hybrid...

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns...

A fuzzy-evolutionary seller agent for an automatic negotiation framework on e-commerce.

Ramón Manjavacas, José Jesús Castro Sánchez, Juan Moreno García (2006)

Mathware and Soft Computing

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The business achievement via e-commerce is getting more important at the present time. E-commerce implies several activities. One of the most significant activity is the consummation of negotiations between sellers and buyers with the aim of reaching agreements. In order to automate these activities the intelligent agent software model is applied. In this work, it is proposed the design of a seller agent for negotiating in competitive frameworks, where many seller agents and a buyer...

Evolution-fuzzy rule based system with parameterized consequences

Piotr Czekalski (2006)

International Journal of Applied Mathematics and Computer Science

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While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps:...

Some practical problems in fuzzy sets-based decision support systems.

Alejandro Sancho-Royo, José Luis Verdegay, Edmundo Vergara-Moreno (1999)

Mathware and Soft Computing

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In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the question, first, of the practical determination of membership functions, second of the management of the fuzziness in some optimisation models, and finally of using fuzzy rules for terminating conventional...

Fuzzy linear programming via simulated annealing

Rita Almeida Ribeiro, Fernando Moura Pires (1999)

Kybernetika

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This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.