.png){kind=icon}
返回
Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s11771-012-1023-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.
Keyword:
estimation of distribution algorithm
fitness function modeling
Gaussian process
surrogate approach
期刊
IF:
4.4
论文数:
5.2K
被引数:
1.0W
机构
暂无机构信息
引用论文
Eliciting compact T-S fuzzy models using subtractive clustering and coevolutionary particle swarm optimization.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Transcranial direct current stimulation on prefrontal and parietal areas enhances motor imageryNeuroReport
NeuroReportIF0
PREAI
分享收藏Fatigue failure predictions for steels in the very high cycle region – A review and recommendations
PREAI
分享收藏分享收藏
查看更多内容