Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison

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Title

Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison

Content partner

Victoria University of Wellington

Collection

Open Access Victoria University of Wellington

Description

Scheduling heuristics are commonly used to solve dynamic scheduling problems in real-world applications. However, designing effective heuristics can be time-consuming and often leads to suboptimal performance. Genetic programming has been widely used to automatically learn scheduling heuristics. In recent years, reinforcement learning has also gained attention in this field. Understanding their strengths and weaknesses is crucial for developing effective scheduling heuristics. This paper take...

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Format

Research paper

Research format

Scholarly text / Journal article

Date created

2024

Creator

Meng Xu / Yi Mei / Fangfang Zhang / Mengjie Zhang

URL

https://figshare.com/articles/journal_contribution/Genetic_Programming_and_Reinforcement_Learning...

Related subjects

Information and computing sciences / Artificial intelligence / Machine learning / Information and Computing Sciences / Artificial Intelligence / Machine Learning / Artificial Intelligence & Image Processing

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