Cross entropy method

May 13th, 2015
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Cross entropy method was first introduced by Reuven Rubinsten in 1997. The cross entropy method is a general Monte Carlo approach which is a wide-ranging class of computational algorithm that rely on repetitive random sampling to attain numerical results

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Literature Review1CHAPTER 2: LITERATURE REVIEW2.1 INTRODUCTIONThis chapter gives emphasize of cross entropy (CE) method. The evolution of CE method is also included. A brief grasp about CE background and concept the CE constitute parts of this chapter as well. Variant of CE and different parameters used for this method are also outline in this chapter. Applications of CE are given before this chapter is concluded. 2.2 EVOLUTION OF CROSS ENTROPYCross entropy method was first introduced by Reuven Rubinsten in 1997. The cross entropy method is a general Monte Carlo approach which is a wide-ranging class of computational algorithm that rely on repetitive random sampling to attain numerical results. It was originated from the field of rare event stimulation, where very small probabilities need to be precisely estimated. The method is revised in 1999 in order solve combinatorial optimization problems. Whilst in 2004, the method is again being modified using an adaptive variance minimization algorithm for rare events of stochastic network in continuous optimization. Cross entropy method is extensively used in optimization problems until now either for discrete or continuous tasks [2]. 2.3 CROSS ENTROPY CONCEPT AND ITS ADVANTAGESIn the main, cross entropy concept is based on iterative procedure which can be break into two phases: 1. Generate a random data sample according to a specified mechanism. 2. Update the parameters of the random mechanismSecond phase is fundamenta

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