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The Fuzzy-Go search engine develops a fuzzy ontology to capture the similarities of terms in the ontology for accomplishing the semantic search of keywords, a web crawler to gather and classify web pages, and a fuzzy search mechanism to aggregate all fuzzy factors based on their degrees of importance and degrees of satisfaction. In this paper, we apply the genetic algorithm to propose a self-adaptation...
The paper adopts the fuzzy c-means text mining method in lots of text mining methods. But aim at the defect that the initial value of the fuzzy c-means is more sensitivity and poor stability, an improved GAFCM text mining method has been put forward. GAFCM uses global search features of genetic algorithms to improve the fuzzy c-means. Finally, it has proved that the improved text mining method has...
In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the...
In this paper, a novel approach is presented to solve the problems of dynamic data mining(DM) such as low effectiveness, high randomness and hard implementation. With the extension of the concept data mining process, the evolutionary and immune characteristics in dynamic mining are first illustrated respectively, based on the facts of the relationship between data mining tasks and the global optimization...
This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The proposed online system belongs...
Fuzzy Rule-Based Classification Systems are a widely used tool in Data Mining because of the interpretability given by the concept of linguistic label. However, the use of this type of models implies a degree of uncertainty in the definition of the fuzzy partitions. In this work we will use the concept of Interval-Valued Fuzzy Set to deal with this problem. The aim of this contribution is to show...
The following topics are dealt with: neural networks; evolutionary computing and genetic algorithms; fuzzy systems and soft computing; particle swarm optimization; artificial life and artificial immune systems; systems biology and neurobiology; support vector machine; rough and fuzzy rough set; knowledge discovery and data mining; kernel methods; supervised & semi-supervised learning; hybrid system;...
War-time transport path optimization is a multi-objective optimization problem. There are some uncertain factors. In order to solve the problem quicker and more effective, this article uses fuzzy math theory and sets up a multi-objective optimization model with the transit-time satisfaction and the arrival traffic satisfaction as the goals. At the same time, the paper designs a new hybrid algorithm...
This paper presents a genetic algorithm (GA) based fuzzy goal programming (FGP) solution method to multiobjective decision making (MODM) problems with fractional criteria. In the model formulation of the problem, first fractional objectives are transformed into fuzzy goals by defining the imprecise aspiration levels to each of them by employing the proposed GA. Then, the concept of membership functions...
Portfolio optimization based on the behavior and risk appetite of the heterogeneous investor community in financial markets has been very difficult to model and predict accurately. In this paper, firstly we attempt to simulate a multi-agent based stock market; where different types of agents are modeled to trade stocks using various strategies. The observations from trading activity of the user are...
The time-cost trade-off problem is a type of the project scheduling problem which studies how to modify project activities so as to achieve the trade-off between the project cost and the completion time. In real projects, the trade-off between project cost and project completion time, and the uncertainty of the environment are both considerable aspects for decision-makers. In this paper, an expected...
A new genetic algorithm based on the theory of lamarckian evolution (Lam-GA) to solve multi-objective transportation optimization problem(MOTP) is presented in the paper. The algorithm carries through some local mutation according to certain rules after distributing transportation counts on the fuzzy rule basis, which can increase the intensity for searching better solution. Experimental data shows...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
The decision-making of normal high water level of a reservoir is a complex and uncertainty problem. To avoid the qualitative and subjective defects of traditional decision-making methods, a novel method by means of fuzzy analytic hierarchy process (FAHP) combined with real coding based accelerating genetic algorithm (RAGA), is described in this paper. A fuzzy preferential relation matrix is established...
The tactile assessments on a set of men's suitings were conducted by an expert panel according to the standardized techniques and processes. Based on the obtained sensory data, a hybrid model integrating the use of fuzzy set theory and genetic algorithms was then developed to study the fuzzy relations between various sensory components and the total fabric hand. In this method, fuzzy comprehensive...
Data mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. It is a process that is developed to examine large amounts of data routinely collected. Fuzzy systems are been used for solving a wide range of problems in different application domain genetic algorithm for designing. Fuzzy systems allows us to introduce the learning and adaptation...
In this paper, we propose a method to construct hedge algebra based type-2 fuzzy logic systems (HA-T2FLS). In these fuzzy logic systems, the footprints of uncertainty (FOU) of type-2 fuzzy sets are optimized by genetic algorithm and the dispersion of data. The key ingredient of our system is the concept of centroid of hedge algebra based type-2 fuzzy sets. It is used in the type-reducing of the HA-T2FLS,...
Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques,...
Natural computing (NC) is a novel approach to solve real life problems inspired in the life itself. A diversity of algorithms had been proposed such as evolutionary techniques, genetic algorithms and particle swarm optimization (PSO). These approaches, together with fuzzy and neural networks, give powerful tools for researchers in a diversity of problems of optimization, classification, data analysis...
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