首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables
Authors:Soloviev  Vicente P  Larrañaga  Pedro  Bielza  Concha
Institution:1.Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, Spain
;
Abstract:

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号