Language model based interactive estimation of distribution algorithm
作者：
Highlights：
• The presented IEDA employs a language model to encode candidate searched items.
• The language model introduces social intelligence and reduces information loss.
• The IEDA adopts Dirichlet-Multinomial distribution as its probabilistic model.
• The probabilistic model is updated with Bayesian learning to track variable.
• Then, a faster personalized search can be expected.
摘要
•The presented IEDA employs a language model to encode candidate searched items.•The language model introduces social intelligence and reduces information loss.•The IEDA adopts Dirichlet-Multinomial distribution as its probabilistic model.•The probabilistic model is updated with Bayesian learning to track variable.•Then, a faster personalized search can be expected.
论文关键词：Estimation of distribution algorithm,Interactive evolutionary algorithm,Language model,Personalized search,Bayesian inference
论文评审过程：Received 23 September 2019, Revised 27 April 2020, Accepted 27 April 2020, Available online 30 April 2020, Version of Record 18 May 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.105980
