Scalability of using Restricted Boltzmann Machines for combinatorial optimization
详细信息 查看全文
- 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
- class="listitem" id="list_celist0002">
- class="label">•
We use a Restricted Boltzmann Machine in an EDA for combinatorial optimization.
- class="label">•
The RBM models variable dependencies and decomposes complex problems into parts.
- class="label">•
RBM–EDA is competitive or superior to state of the art EDAs for four standard benchmark problems.
- class="label">•
The scalability is superior in terms of computation times.