Implementing Genetic Algorithms on Arduino Micro-Controllers
Nuno Alves

TL;DR
This paper demonstrates how genetic algorithms can be effectively implemented on low-power Arduino micro-controllers, expanding their applicability to embedded systems with limited resources.
Contribution
It introduces a practical implementation of genetic algorithms on Arduino Duemilanove, providing open-source libraries for resource-constrained embedded environments.
Findings
Successful implementation on Arduino Duemilanove
Open-source libraries released for public use
Demonstrates feasibility of genetic algorithms on low-end hardware
Abstract
Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimization and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimizations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research · Neural Networks and Applications
