This paper introduces a fuzzy decision tree to initiate the first population of a genetic algorithm to perform data classification. On large datasets, the evolutive process tends to waste computational resources until some good individual is found. It is expected that the use of a fuzzy decision tree can significantly reduce this feature. The genetic algorithm aims to obtain small fuzzy classif...

In fuzzy classi"er systems the classi"cation is obtained by a number of fuzzy If}Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identi"ed automatically. After the projection of the original data set onto a fuzzy space, the optimal subset of...

Let be a simple undirected fuzzy graph. A subset S of V is called a dominating set in G if every vertex in V-S is effectively adjacent to at least one vertex in S. A dominating set S of V is said to be a Independent dominating set if no two vertex in S is adjacent. The independent domination number of a fuzzy graph is denoted by (G) which is the smallest cardinality of a independent dominating ...

In this paper, characterizations of the degree to which a mapping $mathcal{T} : L^{X}longrightarrow M$ is an $(L, M)$-fuzzy topology are studied in detail.What is more, the degree to which an $L$-subset is an $L$-open set with respect to $mathcal{T}$ is introduced.Based on that, the degrees to which a mapping $f: Xlongrightarrow Y$ is continuous,open, closed or a quotient mapping with respect t...

the methods of assessing the individuals’ performance usually applied in practice are based on principles of the bivalent logic (yes-no). however, fuzzy logic, due to its nature of including multiple values, offers a wider and richer field of resources for this purpose. in this paper we use principles of fuzzy logic in developing a new method for assessing the performance of groups of individua...

The methods of assessing the individuals’ performance usually applied in practice are based on principles of the bivalent logic (yes-no). However, fuzzy logic, due to its nature of including multiple values, offers a wider and richer field of resources for this purpose. In this paper we use principles of fuzzy logic in developing a new method for assessing the performance of groups of individua...

This paper is a study on the population dynamics of blowflies employing a density-dependent, non-linear mathematical model and a coupled population formalism. In this study, we investigated the coupled population dynamics applying fuzzy subsets to model the population trajectory, analyzing demographic parameters such as fecundity, survival, and migration. The main results suggest different poss...

In this paper we present an hybrid approach which integrate Fuzzy C-Means (FCM) algorithms and Genetic Algorithms (GAs) to design an optimal classifier for the specific classification problem. This integration allows automatic generation of an classifier system, with an optimized subset of features, from a database of examples. The generated classifier strongly outperform the classic FCM algori...

Significance and relevance of certain features are obtained by various techniques. Feature subset selection involves summarizing mutual associations between class decisions and attribute values in a pre-classified database. In this paper genetic algorithm is used to find the relevant set of features by optimizing the fitness function and using the operators like crossover and mutation. Fuzzy lo...

A fuzzy subset of a universe X (a fuzzy set) is a mathematical object A described by its (generalized) characteristic function (membership function) μA : X → [0, 1] Alternative notation: A(x) In this context, “classical” sets are called crisp or sharp. F(X) denotes the set of all fuzzy subsets of a universe X Range (level set): Range(A) = { α ∈ [0, 1] : (∃x ∈ X : μA(x) = α) } = μA(X) Height: h(...