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17
Synchronization and linearity: an algebra for discrete event systems
, 2001
"... The first edition of this book was published in 1992 by Wiley (ISBN 0 471 93609 X). Since this book is now out of print, and to answer the request of several colleagues, the authors have decided to make it available freely on the Web, while retaining the copyright, for the benefit of the scientific ..."
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Cited by 249 (10 self)
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The first edition of this book was published in 1992 by Wiley (ISBN 0 471 93609 X). Since this book is now out of print, and to answer the request of several colleagues, the authors have decided to make it available freely on the Web, while retaining the copyright, for the benefit of the scientific community. Copyright Statement This electronic document is in PDF format. One needs Acrobat Reader (available freely for most platforms from the Adobe web site) to benefit from the full interactive machinery: using the package hyperref by Sebastian Rahtz, the table of contents and all LATEX crossreferences are automatically converted into clickable hyperlinks, bookmarks are generated automatically, etc.. So, do not hesitate to click on references to equation or section numbers, on items of thetableofcontents and of the index, etc.. One may freely use and print this document for one’s own purpose or even distribute it freely, but not commercially, provided it is distributed in its entirety and without modifications, including this preface and copyright statement. Any use of thecontents should be acknowledged according to the standard scientific practice. The
Fractal Compression of Single Images and Image Sequences using Genetic Algorithms
, 1994
"... In this paper we present a method to encode a single image by finding an Iterated Function System (IFS) that describes an approximation to the image we want to compress by using Genetic Algorithms (GA). The search was restricted to IFS with a fixed number of maps, and a fixed contractivity factor, l ..."
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Cited by 15 (0 self)
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In this paper we present a method to encode a single image by finding an Iterated Function System (IFS) that describes an approximation to the image we want to compress by using Genetic Algorithms (GA). The search was restricted to IFS with a fixed number of maps, and a fixed contractivity factor, like in Barnsley 's brute force method. To improve the convergence for this problem, a modified mating operator was used in the genetic algorithm. By doing this, the time needed to get an IFS is reduced by about 30% compared with Barnsley's brute force method if similar image quality is desired, but more to the point, unlike with other algorithms, using a GA we can vary the time the compression will last as a function of the error we tolerate by varying parameters such as population size and number of generations we allow the algorithm to proceed. The algorithm was extended to deal with image sequences by using the population that has evolved for an image as the initial population for the nex...
DecompositionBased Assembly Synthesis of Multiple Structures for Minimum Production Cost
, 2003
"... An extension of decompositionbased assembly synthesis for structural modularity is presented where the early identification of shareable components within multiple structures is posed as an outcome of the minimization of estimated production costs. The manufacturing costs of components are estimate ..."
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Cited by 10 (4 self)
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An extension of decompositionbased assembly synthesis for structural modularity is presented where the early identification of shareable components within multiple structures is posed as an outcome of the minimization of estimated production costs. The manufacturing costs of components are estimated under given production volumes considering the economies of scale. Multiple structures are simultaneously decomposed and the types of welded joints at component interfaces are selected from a given library, in order to minimize the overall production cost and the reduction of structural strength due to the introduction of joints. A multiobjective genetic algorithm is utilized to allow effective examination of tradeoffs between manufacturing cost and structural strength. A new jointoriented representation of structures combined with a "direct" crossover is introduced to enhance the efficiency of the search. A case study with two aluminum space frame automotive bodies is presented to demonstrate that not all types of component sharing are economically justifiable under a certain production scenario.
Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms
 IEEE Transactions on Fuzzy Systems
, 2003
"... Abstract—In recent years, some methods have been proposed to estimate null values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for ..."
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Cited by 5 (0 self)
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Abstract—In recent years, some methods have been proposed to estimate null values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating null values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers. Index Terms—Fuzzy sets, genetic algorithms (GAs), membership functions, null values, relational database systems, weighted fuzzy rules. Fig. 1. Membership functions of the linguistic terms of the attribute “Salary.” I.
Investigation of Constant Creation Techniques in the context of Gene Expression Programming
, 2004
"... Abstract. Gene Expression Programming (GEP) is a new technique of Genetic Programming (GP) that implements a linear genotype representation. It uses fixedlength chromosomes to represent expression trees of different shapes and sizes, which results in unconstrained search of the genome space while s ..."
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Cited by 4 (1 self)
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Abstract. Gene Expression Programming (GEP) is a new technique of Genetic Programming (GP) that implements a linear genotype representation. It uses fixedlength chromosomes to represent expression trees of different shapes and sizes, which results in unconstrained search of the genome space while still ensuring validity of the program’s output. However, GEP has some difficulty in discovering suitable function structures because the genetic operators are more disruptive than traditional treebased GP. One possible remedy is to specifically assist the algorithm in discovering useful numeric constants. In this paper, the effectiveness of several constant creation techniques for GEP has been investigated through two symbolic regression benchmark problems. Our experimental results show that constant creation methods applied to the whole population for selected generations perform better than methods that are applied only to the best individuals. The proposed tuneup process for the entire population can significantly improve the average fitness of the best solutions. 1
Choosing the Best Set of Bankruptcy Predictors
 In Alander [119
, 1995
"... Discriminant analysis and logit analysis are traditionally used to predict company bankruptcies. More recently, neural networks have been shown to outperform these statistical methods for the problem. The selection of financial indicators as independent variables for all these methods is a major pro ..."
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Cited by 4 (0 self)
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Discriminant analysis and logit analysis are traditionally used to predict company bankruptcies. More recently, neural networks have been shown to outperform these statistical methods for the problem. The selection of financial indicators as independent variables for all these methods is a major problem. In this paper, we let a genetic algorithm operate on a set of variables. The goal is to find the optimal set of financial indicators for the bankruptcy prediction problem. Keywords: Genetic algorithms, financial indicators, bankruptcy prediction, accounting. 1 Introduction Bankruptcy prediction is as interesting today as it was in 1930's when Fitzpatrick 's study was published [Fit32] and during the last 60 years an impressive body of both theoretical and empirical research concerning this topic has evolved. The research focus has been on both finding the best prediction methods and the best failure indicators. From 1966 until 1980's discriminant analysis was the dominant method in ...
Learning restarting automata by genetic algorithms
 SOFSEM 2002: Student research forum, Milovy, Czech Republic
"... Abstract. Restarting automaton is a special type of a linear bounded automaton designed for modelling the socalled analysis by reduction. We use genetic algorithms to learn restarting automata to recognize languages according to input consisting of sets of positive and negative examples of words fr ..."
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Cited by 1 (0 self)
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Abstract. Restarting automaton is a special type of a linear bounded automaton designed for modelling the socalled analysis by reduction. We use genetic algorithms to learn restarting automata to recognize languages according to input consisting of sets of positive and negative examples of words from the language together with positive and negative examples of simplifications. 1
A Comparative Study of Neural Networks in Bankruptcy Prediction
 In Proceedings of the 10th Conference on Artificial Intelligence Research in
, 1994
"... this paper we compare the prediction ability of the backpropagation network, the selforganizing map and the Boltzmann Machine with each other when predicting company bankruptcies. In this comparison we are interested in two aspects. The first is of course prediction accuracy together with the knowle ..."
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this paper we compare the prediction ability of the backpropagation network, the selforganizing map and the Boltzmann Machine with each other when predicting company bankruptcies. In this comparison we are interested in two aspects. The first is of course prediction accuracy together with the knowledge of which financial indicators are the best predictors. The second important thing is that a potential problem should be spotted as early as possible. We will examine the prediction accuracy one, two and three years before bankruptcy. Furthermore, we will use a Genetic Algorithm, an AItechnique based on natural selection, to make a selection of useful indicators from the available models for bankruptcy prdiction. The paper is organised as follows. In the next section we specify four models for bankruptcy prediction. In section III., IV. and V. we briefly describe Kohonen's algorithm, the back propagation model and the Mean Field Annealing algorithm (an algorithm used to train Boltmann Machine networks). Each of these sections will also contain the test results for that specific ANN. In section VI. , we will give a short introduction into Genetic Algorithms and present the results we achieved with this technique. In section VII., we compare the performance of the different approaches and give directions for further research.
Graduate School of Engineering,
"... Abstract—In this research, we considered applying interactive Genetic Algorithm (iGA) to a product recommendation system. Products that suit a user’s preference can be presented by applying iGA to the system and learning the user’s preference. However, if the user’s preference is biased, the depende ..."
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Abstract—In this research, we considered applying interactive Genetic Algorithm (iGA) to a product recommendation system. Products that suit a user’s preference can be presented by applying iGA to the system and learning the user’s preference. However, if the user’s preference is biased, the dependency among design variables should be considered. For this reason, we proposed an offspring generation with consideration for this dependency. In the proposed method, first we apply a clustering technique to the archived individuals which a user selected, and then we construct a Probabilistic Model based on that result in crossover. We plan on examining the effectiveness of the proposed mechanisms by experimenting with iGA for selecting colors and figures of symbols. I.
An Application of Reciprocally induced coevolution: A computational metaphor in Mathematics
"... Natural phenomenon of coevolution is the reciprocally induced evolutionary change between two or more species or population. Though this biological occurrence is a natural fact, there are only few attempts to use this as a simile in computation. This chapter is an attempt to introduce reciprocally i ..."
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Natural phenomenon of coevolution is the reciprocally induced evolutionary change between two or more species or population. Though this biological occurrence is a natural fact, there are only few attempts to use this as a simile in computation. This chapter is an attempt to introduce reciprocally induced coevolution as a mechanism to counter problems faced by a typical genetic algorithm applied as an optimization technique. The domain selected for testing the efficacy of the procedure is the process of finding numerical solutions of Diophantine equations. Diophantine equations are polynomial equations in Mathematics where only integer solutions are sought. Such equations and its solutions are significant in three aspects(i) historically they are important as Hilbert’s tenth problem with a background of more than twenty six centuries;(ii) there are many modern application areas of Diophantine equations like public key cryptography and data dependency in super computers (iii) it has been proved that there does not exist any general method to find solutions of such equations. The proposed procedure has been tested with Diophantine equations with varied powers and varied number of variables.