Real-coded Crossovers as a Role of Kernel Density Estimator Proposal of Crossover Kernels based on Unimordal Normal Distribution Crossover

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  • Sakuma Jun
    Interdisciplinary Graduate school of Science and Engineering, Tokyo Institute of Technology
  • Kobayashi Shigenobu
    Interdisciplinary Graduate school of Science and Engineering, Tokyo Institute of Technology

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Other Title
  • カーネル密度推定器としての実数値交叉　　ＵＮＤＸに基づく交叉カーネルの提案
  • カーネル ミツド スイテイキ ト シテノ ジッスウチ コウサ UNDX ニ モトズク コウサ カーネル ノ テイアン
  • Proposal of Crossover Kernels based on Unimordal Normal Distribution Crossover
  • UNDXに基づく交叉カーネルの提案

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Abstract

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel density estimator. We also propose an application of crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures.

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