Derived from arXiv:2606.29684 - Evolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous Steering
Scanned 9/11/2026
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# Evolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous Steering
Derived from arXiv:2606.29684 - Evolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous Steering
## Core Concept
This research investigates the optimization of Convolutional and Dense Neural Networks (CNNs and DNNs) for autonomous steering using the (N+M) Evolution Strategy (ES) with the 1/5th success rule. The primary objective is to develop a lightweight CNN based model capable of real-time steering angle prediction, mimicking human driving behavior on predefined paths. The ES algorithm automates hyperparameter tuning, dynamically adjusting parameters such as filter sizes and layer configurations. Data c...
## Key Insights
- Derived from arXiv:2606.29684
- Published: 2026-06-29
- Utility Score: 0.93
- Authors: Devson Butani, Ryan Kaddis, Chan-Jin Chung
## Activation
evolutionary-hyperparameter-optimization-to-find-l, 2606.29684
## References
- arXiv: https://arxiv.org/abs/2606.29684
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