.png){kind=icon}
返回
Simulating non-stationary operators in search algorithms
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2015.09.024.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose new scenarios for simulating search operators whose behaviors often change continuously during the search. In these scenarios, the performance of such operators decreases while they are applied. This is motivated by the fact that operators for optimization problems are often roughly classified into exploitation and exploration operators. Our simulation model is used to compare the performances of operator selection policies and to identify their ability to handle specific non-stationary operators. An experimental study highlights respective behaviors of operator selection policies when faced to such non-stationary search scenarios. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Adaptive operator selection
Operator based algorithms
Multi armed bandit
Island models
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
暂无机构信息
引用论文
Susceptibility of different mouse strains to oxaliplatin peripheral neurotoxicity: Phenotypic and genotypic insights.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Reinforcement learning and evolutionary algorithms for non-stationary multi-armed bandit problems
PREAI
分享收藏
查看更多内容