Scalability of using Restricted Boltzmann Machines for combinatorial optimization
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- 作者：Malte Probst ; a ; probst@uni-mainz.de" class="auth_mail" title="E-mail the corresponding author ; ; Franz Rothlaufa ; rothlauf@uni-mainz.de" class="auth_mail" title="E-mail the corresponding author ; Jö ; rn Grahlb ; grahl@wiso.uni-koeln.de" class="auth_mail" title="E-mail the corresponding author ;
- 关键词：Combinatorial optimization ; Heuristics ; Evolutionary computation ; Estimation of Distribution Algorithms ; Neural Networks
- 刊名：European Journal of Operational Research
- 出版年：2017
- 出版时间：16 January 2017
- 年：2017
- 卷：256
- 期：2
- 页码：368-383
- 全文大小：951 K
文摘
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We use a Restricted Boltzmann Machine in an EDA for combinatorial optimization.
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The RBM models variable dependencies and decomposes complex problems into parts.
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RBM–EDA is competitive or superior to state of the art EDAs for four standard benchmark problems.
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The scalability is superior in terms of computation times.