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A dilemma for fitness sharing with a scaling function
 in Proc. of the 1995 IEEE Int'l Conf. on Evolutionary Computation (ICEC'95
, 1995
"... Fitness sharing has been used widely in genetic algorithms for multiobjective function optimization and machine learning. It is often implemented with a scaling function, which adjusts an individual’s raw fitness to improve the performance of the genetic algorithm. However, choosing a scaling func ..."
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Cited by 17 (10 self)
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Fitness sharing has been used widely in genetic algorithms for multiobjective function optimization and machine learning. It is often implemented with a scaling function, which adjusts an individual’s raw fitness to improve the performance of the genetic algorithm. However, choosing a scaling function is an ad hoc affair that lacks sufficient theoretical foundation. Although this is already known, an explanation of why scaling works is lacking. This paper explains why a scaling function is often needed for fitness sharing. We investigate fitness sharing’s performance at multiobjective optimization, demonstrate t.he need for a scaling function of some kind, and discuss what form of scaling function would be best. We provide both theoretical and empirical evidence that fitness sharing with a scaling function suffers a dilemma which can easily be mistaken for decept,ion. Our theoretical analyses and empirical studies explain why a largerthannecessary population is needed for fitness sharing with a scaling function to work, and give an explanation for common fixes such as further processing with a hillclimbing algorithm. Our explanation predicts that annealing the scaling power during a run will improve results, and we verify that it does. I.
Intelligent Mining in Image Databases, With Applications to Satellite Imaging and to Web Search
 Data Mining and Computational Intelligence
, 2000
"... An important part of our knowledge is in the form of images. For example, a large amount of geophysical and environmental data comes from satellite photos; a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in da ..."
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Cited by 13 (5 self)
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An important part of our knowledge is in the form of images. For example, a large amount of geophysical and environmental data comes from satellite photos; a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most existing data mining techniques have been designed for mining numerical data and are thus not well suited for image databases; so, new methods are needed for image mining. In this paper, we show how data mining can be used to find common patterns in several images. 1 Introduction 1.1 It is necessary to apply data mining to images An important part of our knowledge is in the form of images. For example, a large amount of geophysical and environmental data comes from satellite photos; a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most exis...
Aircraft Integrity and Reliability
, 2000
"... . In his recent paper "Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems", L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems. In this chapter, we gi ..."
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Cited by 4 (4 self)
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. In his recent paper "Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems", L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems. In this chapter, we give an example of a realworld problem for which such an infusion is indeed successful: the problems of aircraft integrity and reliability. 1 Case Study: Aircraft Structural Integrity. Formulation of the Problem 1.1 Aerospace Testing: Why One of the most important characteristics of the plane is its weight: every pound shaved off the plane means a pound added to the carrying ability of this plane. As a result, planes are made as light as possible, with their "skin" as thin as possible. However, the thinner the layer, the more vulnerable is the resulting 1 structure to stresses and faults, and a flight is a very stressful experience. Therefore, even minor faults in the plane's structure, if undetect...
Genetic algorithm for hierarchical wireless sensor networks
 J. Networks (JNW
"... Abstract — Large scale wireless sensor networks (WSNs) can be used for various pervasive and ubiquitous applications such as security, healthcare, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, ..."
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Cited by 1 (0 self)
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Abstract — Large scale wireless sensor networks (WSNs) can be used for various pervasive and ubiquitous applications such as security, healthcare, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA) is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments. Index Terms — sensor networks, hierarchical, genetic algorithm, routing, energy efficient I.
Interval Computations No 2, 1994 Interval Approach to Testing Software
"... The experience of many programmers shows that it is actually impossible to extract all the faults from a sophisticated software. Therefore it is necessary to estimate the time interval during which the remaining faults will not influence the program. The statistical models which are used to get such ..."
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The experience of many programmers shows that it is actually impossible to extract all the faults from a sophisticated software. Therefore it is necessary to estimate the time interval during which the remaining faults will not influence the program. The statistical models which are used to get such estimates are semiheuristic and lack convincing theoretical explanation. In this paper, we consider an interval approach to program testing. We formulate the problem of choosing the best interval software reliability model as a mathematical optimization problem, and solve this problem. Интервальный подход к тестированию программного обеспечения
CHAPTER 10 Aircraft Integrity and Reliability
"... Abstract. In his recent paper “Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems”, L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems. In this chapter, we give ..."
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Abstract. In his recent paper “Probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems”, L. A. Zadeh explains that probability theory needs an infusion of fuzzy logic to enhance its ability to deal with realworld problems. In this chapter, we give an example of a realworld problem for which such an infusion is indeed successful: the problems of aircraft integrity and reliability.
A NEW CHARACTERIZATION OF THE SET OF ALL INTERVALS, BASED ON THE NECESSITY TO CHECK CONSISTENCY EASILY
"... Abstract. The purpose of this paper is to present a new characterization of the set of all intervals. This characterization is based on several natural properties useful in mathematical modeling; the main of these properties is the necessity to easily check consistency of incomplete knowledge. This ..."
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Abstract. The purpose of this paper is to present a new characterization of the set of all intervals. This characterization is based on several natural properties useful in mathematical modeling; the main of these properties is the necessity to easily check consistency of incomplete knowledge. This characterization is obtained both for onedimensional and for multidimensional cases.
Artillery Academy of People Liberation Army,
"... This paper discusses using genetic algorithms (GA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing ..."
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This paper discusses using genetic algorithms (GA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing the genetic algorithm, a novel encoding method is proposed for optimizing the RBPNN structure. This encoding method can not only make the selected hidden centers sufficiently reflect the key distribution characteristic in the space of training samples set and reduce the hidden centers number as few as possible, but also simultaneously determine the optimum controlling parameters of Gaussian kernel functions matching the selected hidden centers. Additionally, we also constructively propose a new fitness function so as to make the designed RBPNN as simple as possible in the network structure in the case of not losing the network performance. Finally, we take the two benchmark problems of discriminating twospiral problem and classifying the iris data, for example, to test and evaluate this designed GA. The experimental results illustrate that our designed GA can significantly reduce the required hidden centers number, compared with the recursive orthogonal least square algorithm (ROLSA) and the modified Kmeans algorithm (MKA). In particular, by means of statistical experiments it was proved that the optimized RBPNN by our designed GA, have still a better generalization performance with respect to the ones by the ROLSA and the MKA, in spite of the network scale having been greatly reduced. Additionally, our experimental results also demonstrate that our designed GA is also suitable for optimizing the radial basis function neural networks (RBFNN).
Nonlinear Rescaling Of Control Values Simplifies Fuzzy Control
, 1992
"... Traditional control theory is welldeveloped mainly for linear control situations. In nonlinear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their kn ..."
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Traditional control theory is welldeveloped mainly for linear control situations. In nonlinear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts'