Q-learning and hyper-heuristic based algorithm recommendation for changing environments
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Q-learning and hyper-heuristic based algorithm recommendation for changing environments
Authors
Keywords
Dynamic optimization, Hyper-heuristics, Q-learning, Multidimensional knapsack problem
Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 102, Issue -, Pages 104284
Publisher
Elsevier BV
Online
2021-05-11
DOI
10.1016/j.engappai.2021.104284
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Evolutionary and adaptive inheritance enhanced Grey Wolf Optimization algorithm for binary domains
- (2020) İlker Gölcük et al. KNOWLEDGE-BASED SYSTEMS
- Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications
- (2020) Laith Abualigah NEURAL COMPUTING & APPLICATIONS
- A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications
- (2020) Laith Abualigah et al. NEURAL COMPUTING & APPLICATIONS
- A parallel hybrid krill herd algorithm for feature selection
- (2020) Laith Abualigah et al. International Journal of Machine Learning and Cybernetics
- A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series
- (2020) Seçkin Karasu et al. ENERGY
- A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
- (2020) Aytaç Altan et al. APPLIED SOFT COMPUTING
- Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics
- (2019) H. Mosadegh et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Artificial search agents with cognitive intelligence for binary optimization problems
- (2019) Fehmi Burcin Ozsoydan COMPUTERS & INDUSTRIAL ENGINEERING
- Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications
- (2019) Weiguo Zhao et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Hybrid clustering analysis using improved krill herd algorithm
- (2018) Laith Mohammad Abualigah et al. APPLIED INTELLIGENCE
- A dual-population multi operators harmony search algorithm for dynamic optimization problems
- (2018) Ayad Turky et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Dynamic optimization in binary search spaces via weighted superposition attraction algorithm
- (2018) Adil Baykasoğlu et al. EXPERT SYSTEMS WITH APPLICATIONS
- Analysis of selection hyper-heuristics for population-based meta-heuristics in real-valued dynamic optimization
- (2018) Stefan A.G. van der Stockt et al. Swarm and Evolutionary Computation
- Quantum firefly swarms for multimodal dynamic optimization problems
- (2018) Fehmi Burcin Ozsoydan et al. EXPERT SYSTEMS WITH APPLICATIONS
- Adaptive recommendation model using meta-learning for population-based algorithms
- (2018) Xianghua Chu et al. INFORMATION SCIENCES
- Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization
- (2017) Adil Baykasoğlu et al. INFORMATION SCIENCES
- The Whale Optimization Algorithm
- (2016) Seyedali Mirjalili et al. ADVANCES IN ENGINEERING SOFTWARE
- Heuristic space diversity control for improved meta-hyper-heuristic performance
- (2015) Jacomine Grobler et al. INFORMATION SCIENCES
- Population-based Algorithm Portfolios with automated constituent algorithms selection
- (2014) Ke Tang et al. INFORMATION SCIENCES
- An improved firefly algorithm for solving dynamic multidimensional knapsack problems
- (2013) Adil Baykasoğlu et al. EXPERT SYSTEMS WITH APPLICATIONS
- Selection hyper-heuristics in dynamic environments
- (2013) B Kiraz et al. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
- Hyper-heuristics: a survey of the state of the art
- (2013) Edmund K Burke et al. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
- A hybrid multi-population framework for dynamic environments combining online and offline learning
- (2013) Gönül Uludağ et al. SOFT COMPUTING
- A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems
- (2012) M.M. Lotfi et al. APPLIED SOFT COMPUTING
- Evolutionary dynamic optimization: A survey of the state of the art
- (2012) Trung Thanh Nguyen et al. Swarm and Evolutionary Computation
- A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks
- (2011) C. Chitra et al. EXPERT SYSTEMS WITH APPLICATIONS
- A T-cell algorithm for solving dynamic optimization problems
- (2011) Victoria S. Aragón et al. INFORMATION SCIENCES
- A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
- (2011) Joaquín Derrac et al. Swarm and Evolutionary Computation
- A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
- (2008) Hongfeng Wang et al. SOFT COMPUTING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search