H2-SLAN: A hyper-heuristic based on stochastic learning automata network for obtaining, storing, and retrieving heuristic knowledge
作者：
Highlights：
• We present a proposal of a hyper-heuristic model with learning based on stochastic automata networks.
• The model introduces new learning settings (on-line, off-line, and no learning).
• We introduce a Hyper-heuristic that is free from the abstractions employed by meta-heuristics.
• The model contributes to the reduction of the computational time spent in the optimization process.
• The model presents a different approach that allows configuration based on regular expressions.
摘要
•We present a proposal of a hyper-heuristic model with learning based on stochastic automata networks.•The model introduces new learning settings (on-line, off-line, and no learning).•We introduce a Hyper-heuristic that is free from the abstractions employed by meta-heuristics.•The model contributes to the reduction of the computational time spent in the optimization process.•The model presents a different approach that allows configuration based on regular expressions.
论文关键词：Hyper-heuristics,Meta-heuristics,On-line learning,Operations research
论文评审过程：Received 18 October 2019, Revised 20 January 2020, Accepted 30 March 2020, Available online 4 April 2020, Version of Record 22 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113426
