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In this paper, we describe three new soft computing methods for segmentation of both gray level and color images by using a fuzzy entropy based cost function for the genetic algorithm. The presented methods allow us to find optimized set of parameters for a predefined cost function. Particularly, we found the optimum set of membership functions by maximizing the fuzzy entropy and based on the membership...
In this paper, an effective multi-threshold image segmentation method is proposed based on the measure of an adaptive fuzzy maximum entropy. In the traditional image segmentation algorithms with fuzzy entropy, C-threshold is usually determined by 2*C parameters at least, which are generally searched by a conventional genetic algorithm (GA) or simulated anneal algorithm (SA). Adaptive fuzzy entropy...
In order to alleviate user fatigue of interactive genetic algorithms with an individual's fuzzy and stochastic fitness, we propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract user cognition. According to cut-set level and interval dominance probability, we present approaches to construct a directed fuzzy graph of an evolutionary population and calculate an individual's...
In order to improve the convergence rate of the genetic algorithm based on edge detection, a novel edge detection method based on good point set genetic algorithm(GGA) was proposed. The proposed method first redesigns the crossover operation by using the theory of good point set in which progeny inherits the common genes of parents which represent its family so as to improve the convergence rate of...
This paper deals with automatic image thresholding based on fuzzy entropy definition. It is used to select the fuzzy region of membership function so that an image is able to be transformed into fuzzy domain with maximum fuzzy entropy. Then we are able to divide the fuzzy region and establish the thresholds. For selection of optimal membership function is used genetic algorithm.
Fuzzy extension matrix (FEM) inductive learning is an important method that generates knowledge from cases. Compared with conventional extension matrix techniques, it is more powerful and practical to handle with ambiguities in classification problems. Rule extraction from fuzzy extension matrix involves three parameters alpha, beta and gamma. These parameters play an importation role in the entire...
In this paper, a fuzzy modeling method using genetic algorithms (GAs) with a conciseness measure is presented. This paper introduces De Luca and Termini's fuzzy entropy to evaluate the shape of a membership function, and proposes another measure to evaluate the deviation of a membership function from symmetry. A combined measure is then derived from these two measures, and a new conciseness measure...
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