Design of fuzzy classification system using genetic algorithms

[[abstract]]This paper proposes a GA-based method to construct an appropriate fuzzy classification system to maximize the number of correctly classified patterns and minimize the number of fuzzy rules. In this method, a fuzzy classification system is coded as an individual in the GA. A fitness function is defined such that it can guide the search procedure to select an appropriate fuzzy classification system to maximize the number of correctly classified patterns and minimize the number of fuzzy rules. Finally, a two-class classification problem is utilized to illustrate the efficiency of the proposed method[[conferencetype]]國際[[conferencedate]]20000507~20000510[[iscallforpapers]]Y[[conferencelocation]]San Antonio, TX, US

K-means-based fuzzy classifier design

[[abstract]]In this paper, a method based on the K-means algorithm is proposed to efficiently design a fuzzy classifier so that the training patterns can be correctly classified by the proposed approach. In this method, the K-means algorithm is first used to partition the training data for each class into several clusters, and the cluster center and the radius for each cluster are calculated. Then, a fuzzy system design method that uses a fuzzy rule to represent a cluster is proposed such that a fuzzy classifier can be efficiently constructed to correctly classify the training data. The proposed method has the following features: 1) it does not need prior parameter definition; 2) it only needs a short training time; and 3) it is simple. Finally, two examples are used to illustrate and examine the proposed method for the fuzzy classifier design[[conferencetype]]國際[[conferencedate]]20000507~20000507[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]San Antonio, TX, US

Fuzzy Rules Optimization in Fuzzy Expert System for Machinability Data Selection: Genetic Algorithms Approach

Machinability data selection is complex and cannot be easily formulated by any mathematical model to meet design specification. Fuzzy logic is a good approach to solve such problems. Fuzzy rules optimization is always a problems for a complex fuzzy rules from more than 10 thousand combinations. (Wong et aL 1997) developed fuzzy models for machinability data selection. There are more than 2 x 1029 possible sets of rules for each model. Situation would be more complicated if further increase the number of inputs and/or outputs. The fuzzy rules were selected by trial and error and intuition in reference (Wong et aL 1997). Genetic optimization is suggested in this paper to further optimizing the fuzzy rules optimization with genetic algorithms has been developed. Weighted centroid method is used for output defuzzi fication to save processing time. Comparisons between the results of the new models and the previously published literatures are made

SELEÇÃO DE INSTÂNCIAS DE GRANDES BASES DE DADOS USANDO ALGORITMOS EVOLUTIVOS MULTIOBJETIVO

Os Sistemas Baseados em Regras Fuzzy (SBRF) têm sido amplamente usados para a resolução de diversos tipos de problemas, tais como, controle (Leephakpreeda, 2011), modelagem (Pedrycz, 1996), classificação (Ishibuci, 1995). A maneira mais comum para a aquisição do conhecimento de um SBRF é a partir de dados numéricos, os quais representam amostras ou exemplos do problema. As formas mais bem-sucedidas de extração automática de conhecimento a partir de dados para a construção de SFBR são as que combinam metodologias para aprendizado de máquina com conceitos de sistemas fuzzy. Entre elas, destacam-se as Redes Neurais Artificiais e a Computação Evolutiva (Cordón et al, 2001).Os Algoritmos Genéticos Multiobjetivo (AGMO), vêm demonstrando ser uma poderosa ferramenta para a construção automática (ou projeto automático) de SBRF. No entanto, este processo é fortemente influenciado pela quantidade de instâncias e características presentes nas bases de dados, que afetam o tamanho do espaço de busca e o tempo computacional. Por isso, a redução de dados é de fundamental importância para reduzir o tempo de aprendizado do SBRF e amenizar as dificuldades durante o processo de convergência dos algoritmos evolutivos.A redução de dados, neste caso, a seleção de instâncias, é um problema multiobjetivo, pois busca-se reduzir a base de dados, e ao mesmo tempo, manter o desempenho do classificador estável ou superior, quando comparado com a base de dados original. Portanto, este trabalho possui o objetivo de investigar a aplicação de Algoritmos Genéticos Multiobjetivo para a seleção de instâncias de grandes bases de dados

EVALUATING SCHEDULING METHODS FOR ENERGY COST REDUCTION IN A HETEROGENEOUS DATA CENTER ENVIRONMENT

Over the past two decades the rise of information technologies (IT) has enabled businesses to communicate, coordinate, and cooperate in unprecedented ways. However, this did not come without a price. Today, IT infrastructure accounts for a substantial fraction of the national energy consumption in most advanced countries. Subsequently, research turned to finding ways of making IT more sustainable and lessening the environmental impact of IT infrastructure. In our previous work we developed LINFIX, an innovative method for handling the scheduling problem in data centers, which substantially reduced the total energy consumption compared to commonly used practices. Due to the computational complexity of the scheduling problem, we were, however, unable to estimate the cost reduction of LINFIX compared to what is theoretically possible. In this work we employ a genetic algorithm to provide a benchmark to better assess the quality of the LINFIX solutions. While the genetic algorithm frequently finds better solutions, the additional average cost reduction when compared to LINFIX is less than 0.1 percent. Taking the computational speed into account, this confirms our hypothesis that LINFIX provides very energy efficient scheduling plans in short time