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Deep reinforcement learning based parameter control in differential evolution

https://doi.org/10.1145/3321707.3321813
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-31306-0openalex-snapshot
Differential Evolution: A Practical Approach to Global Optimization
inferredcites →https://doi.org/10.1007/978-3-319-16549-3_54openalex-snapshot
Evaluating Reward Definitions for Parameter Control
inferredcites →https://doi.org/10.1007/978-3-319-99259-4_26openalex-snapshot
Performance Assessment of Recursive Probability Matching for Adaptive Operator Selection in Differential Evolution
inferredcites →https://doi.org/10.1007/978-3-540-69868-5_10openalex-snapshot
Reinforcement Learning for Online Control of Evolutionary Algorithms
inferredcites →https://doi.org/10.1007/978-3-642-15844-5_20openalex-snapshot
Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution
inferredcites →https://doi.org/10.1007/978-3-642-29178-4_37openalex-snapshot
A Generic Approach to Parameter Control
inferredcites →https://doi.org/10.1016/j.jspi.2007.04.032openalex-snapshot
A two-step rejection procedure for testing multiple hypotheses
inferredcites →https://doi.org/10.1023/a:1008202821328openalex-snapshot
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/cec.2005.1554902openalex-snapshot
A Restart CMA Evolution Strategy With Increasing Population Size
inferredcites →https://doi.org/10.1109/cec.2005.1554903openalex-snapshot
Performance Evaluation of an Advanced Local Search Evolutionary Algorithm
inferredcites →https://doi.org/10.1109/cimca.2005.1631422openalex-snapshot
SCGA: Controlling Genetic Algorithms with Sarsa(0)
inferredcites →https://doi.org/10.1109/sitis.2010.22openalex-snapshot
A Method to Control Parameters of Evolutionary Algorithms by Using Reinforcement Learning
inferredcites →https://doi.org/10.1109/tevc.2014.2308294openalex-snapshot
Parameter Control in Evolutionary Algorithms: Trends and Challenges
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1145/1143997.1144086openalex-snapshot
A comparative study of differential evolution variants for global optimization
inferredcites →https://doi.org/10.1145/1830483.1830559openalex-snapshot
Adaptive strategy selection in differential evolution
inferredcites →https://doi.org/10.1145/1830483.1830619openalex-snapshot
Toward comparison-based adaptive operator selection
inferredcites →https://doi.org/10.1145/2576768.2598360openalex-snapshot
Generic parameter control with reinforcement learning
inferredcites →https://doi.org/10.1145/2996355openalex-snapshot
A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms
inferredcites →https://doi.org/10.1609/aaai.v30i1.10295openalex-snapshot
Deep Reinforcement Learning with Double Q-Learning
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization

What points at this work

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inferred← citeshttps://doi.org/10.2139/ssrn.4815390openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-030-58112-1_48openalex-snapshot
Learning Step-Size Adaptation in CMA-ES
inferred← citeshttps://doi.org/10.1007/978-3-030-91702-9_19openalex-snapshot
Online Selection of Heuristic Operators with Deep Q-Network: A Study on the HyFlex Framework
inferred← citeshttps://doi.org/10.1007/978-3-032-09156-7_13openalex-snapshot
Deep Reinforcement Learning Based Genetic Framework for Flexible Job-Shop Scheduling Under Practical Constraints
inferred← citeshttps://doi.org/10.1016/j.cor.2024.106643openalex-snapshot
Automated placement of analog integrated circuits using priority-based constructive heuristic
inferred← citeshttps://doi.org/10.1016/j.swevo.2024.101644openalex-snapshot
Continuous variation operator configuration for decomposition-based evolutionary multi-objective optimization
inferred← citeshttps://doi.org/10.1016/j.swevo.2026.102535openalex-snapshot
Landscape-driven stochastic variation: A Data-Driven Meta-Learning Framework for Adaptive Mutation Control (DD-AMC) in constrained evolutionary multi-objective optimisation
inferred← citeshttps://doi.org/10.1109/docs70981.2026.11698640openalex-snapshot
Reinforcement Learning-Based Requirement-Driven Adaptive Differential Evolution for Multitask Optimization
inferred← citeshttps://doi.org/10.1613/jair.1.13922openalex-snapshot
Automated Dynamic Algorithm Configuration
inferred← citeshttps://doi.org/10.3390/drones10100721openalex-snapshot
Group-Level Heterogeneous Reinforcement-Learning-Guided Jellyfish Search Optimization for Cooperative Multi-UAV Path Planning Under Dynamic and Adversarial Conditions
inferred← citeshttps://doi.org/10.70675/69eb2c60z998cz4ff6zaef7z4d002022bc97openalex-snapshot
Applications of machine learning to the resolution of recurrent combinatorial optimization problems
inferred← citeshttps://doi.org/10.70675/903277a0za4f9z4f32z8ab6zdb11b19f660dopenalex-snapshot
Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization
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