Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the sel...
Saved in:
Main Authors: | HANDOKO, Stephanus Daniel, Nguyen, Duc Thien, YUAN, Zhi, LAU, Hoong Chuin |
---|---|
格式: | text |
語言: | English |
出版: |
Institutional Knowledge at Singapore Management University
2014
|
主題: | |
在線閱讀: | https://ink.library.smu.edu.sg/sis_research/2666 https://ink.library.smu.edu.sg/context/sis_research/article/3666/viewcontent/ReinforcementLearningAdaptiveOperSelQAP_2014_GECCO.pdf |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Singapore Management University |
語言: | English |
相似書籍
-
Building algorithm portfolios for memetic algorithms
由: MISIR, Mustafa, et al.
出版: (2014) -
Hybrid Metaheuristics for Solving the Quadratic Assignment Problem and the Generalized Quadratic Assignment Problem
由: GUNAWAN, Aldy, et al.
出版: (2014) -
OSCAR: Online selection of algorithm portfolios with case study on memetic algorithms
由: MISIR, Mustafa, et al.
出版: (2015) -
An Empirical Study of Off-line Configuration and On-line Adaptation in Operator Selection
由: YUAN, Zhi, et al.
出版: (2014) -
Self-organizing neural network for adaptive operator selection in evolutionary search
由: TENG, Teck Hou, et al.
出版: (2016)