Summary of Configx: Modular Configuration For Evolutionary Algorithms Via Multitask Reinforcement Learning, by Hongshu Guo et al.
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
by Hongshu Guo, Zeyuan Ma, Jiacheng Chen, Yining Ma, Zhiguang Cao, Xinglin Zhang, Yue-Jiao Gong
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Recent advancements in Meta-learning for Black-Box Optimization (MetaBBO) have enabled the dynamic configuration of evolutionary algorithms (EAs) using neural networks, enhancing their performance and adaptability. However, these approaches are often tailored to specific EAs, limiting their generalizability. To address this limitation, we introduce ConfigX, a MetaBBO framework that learns a universal configuration agent for boosting diverse EAs. Our approach leverages a novel modularization system and a Transformer-based neural network to meta-learn a universal configuration policy through multitask reinforcement learning. Extensive experiments demonstrate that ConfigX achieves robust zero-shot generalization to unseen tasks, outperforming state-of-the-art baselines, and exhibits strong lifelong learning capabilities for efficient adaptation to new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have been working on ways to improve the performance of optimization algorithms using artificial intelligence. They’ve made progress in “Meta-learning” which helps configure these algorithms better. However, this approach often requires retraining or redesigning for different problems. To fix this, scientists introduced a new method called ConfigX that can learn how to boost many types of optimization algorithms at once. This was achieved by combining different parts of an algorithm together and using a special kind of AI network to learn from multiple tasks simultaneously. The results show that ConfigX works well on new, unseen problems and gets better over time. |
Keywords
» Artificial intelligence » Boosting » Generalization » Meta learning » Neural network » Optimization » Reinforcement learning » Transformer » Zero shot
