A tutorial on the cross-entropy method

被引:2101
作者
De Boer, PT
Kroese, DP
Mannor, S
Rubinstein, RY
机构
[1] Univ Twente, Dept Elect Engn Math & Comp Sci, NL-7500 AE Enschede, Netherlands
[2] Univ Queensland, Dept Math, Brisbane, Qld 4072, Australia
[3] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada
[4] Technion Israel Inst Technol, Dept Ind Engn, IL-32000 Haifa, Israel
基金
以色列科学基金会;
关键词
cross-entropy method; Monte-Carlo simulation; randomized optimization; machine learning; rare events;
D O I
10.1007/s10479-005-5724-z
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal 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 applications in combinatorial optimization and machine learning.
引用
收藏
页码:19 / 67
页数:49
相关论文
共 50 条
  • [1] Aarts E., 1989, Wiley-Interscience Series in Discrete Mathematics and Optimization
  • [2] Alon G., 2005, ANN OPER RES, V134, P19
  • [3] [Anonymous], 1989, GENETIC ALGORITHM SE
  • [4] [Anonymous], 1979, Computers and Intractablity: A Guide to the Theoryof NP-Completeness
  • [5] ASMUSSEN S, 2005, IN PRESS STOCHASTIC, V21
  • [6] NEURONLIKE ADAPTIVE ELEMENTS THAT CAN SOLVE DIFFICULT LEARNING CONTROL-PROBLEMS
    BARTO, AG
    SUTTON, RS
    ANDERSON, CW
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1983, 13 (05): : 834 - 846
  • [7] BARTON AG, 1998, REINFORCEMENT LEARNI
  • [8] Experiments with infinite-horizon, policy-gradient estimation
    Baxter, J
    Bartlett, PL
    Weaver, L
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2001, 15 : 351 - 381
  • [9] Bertsekas DP, 2012, DYNAMIC PROGRAMMING, V2
  • [10] BERTSEKAS DP, 1995, NEURODYNAMIC PROGRAM