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A Tutorial on the CrossEntropy Method
, 2005
"... The crossentropy (CE) method is a new generic approach to combinatorial and multiextremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss a ..."
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Cited by 179 (18 self)
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The crossentropy (CE) method is a new generic approach to combinatorial and multiextremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss
The CrossEntropy Method for Optimization
"... The crossentropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or crossentropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how the cr ..."
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Cited by 4 (2 self)
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The crossentropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or crossentropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how
Using CrossEntropy for Satisfiability ∗
"... This paper proposes a novel approach to SAT solving by using the crossentropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multivalued framework, where a probability space is induced over the set of all possible assignments. For a given formula, a ..."
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This paper proposes a novel approach to SAT solving by using the crossentropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multivalued framework, where a probability space is induced over the set of all possible assignments. For a given formula
Generalized CrossEntropy Methods
"... The crossentropy and minimum crossentropy methods are wellknown Monte Carlo simulation techniques for rareevent probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general nonparametric crossentropy framework based on φdivergenc ..."
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Cited by 2 (0 self)
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The crossentropy and minimum crossentropy methods are wellknown Monte Carlo simulation techniques for rareevent probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general nonparametric crossentropy framework based on φ
The CrossEntropy Method for Estimation
"... This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the crossentropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the crossentropy or Kullback–Leibler divergence as a measure of closenes ..."
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This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the crossentropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the crossentropy or Kullback–Leibler divergence as a measure
Crossentropy based testing
 In FMCAD
, 2007
"... Abstract—In simulationbased verification, we check the correctness of a given program by executing it on some input vectors. Even for mediumsize programs, exhaustive testing is impossible. Thus, many errors are left undetected. The problem of increasing the exhaustiveness of testing and decreasin ..."
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Cited by 3 (1 self)
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and decreasing the number of undetected errors is the main problem of software testing. In this paper, we present a novel approach to software testing, which allows us to dramatically raise the probability of catching rare errors in large programs. Our approach is based on the crossentropy method. We define a
The cross entropy method for classification
"... We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cross entropy method to search over the possib ..."
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Cited by 2 (0 self)
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We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cross entropy method to search over
PARALLEL CROSSENTROPY OPTIMIZATION
"... The CrossEntropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a gener ..."
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The CrossEntropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a
CrossEntropy and Linguistic Typology
 Proceedings of New Methods in Language Processing 3
, 1998
"... The idea of "familial relationships" among languages is wellestablished and accepted, although some controversies persist in a few specific instances. By painstakingly recording and identifying regularities and similarities and comparing these to the historical record, linguists hav ..."
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Cited by 7 (1 self)
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that KullbackLeibler divergence (or crossentropy) can be meaningfully measured from small samples, in some cases as small as only 20 or so words. Using these techniques, we define and measure a distance function between translations of a small corpus (c. 70 words/sample) covering much of the accepted
Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross entropy
 IEEE Trans. Information Theory
, 1980
"... dple of min imum cromentropy (mhlmum dire&d dfvergenoe) are shown tobeunfquelycomxtmethodsforhductiveinf~whennewinformnt ionlsghninthefomlofexpe&edvalues.ReviousjILstit icatioaslLve ..."
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Cited by 279 (3 self)
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dple of min imum cromentropy (mhlmum dire&d dfvergenoe) are shown tobeunfquelycomxtmethodsforhductiveinf~whennewinformnt ionlsghninthefomlofexpe&edvalues.ReviousjILstit icatioaslLve
Results 1  10
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