2026-05-30T19:32:09Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/384732026-04-20T09:08:38Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Cotta-Porras, Carlos
author
Martínez-Cruz, Jesús
2025-04-24T09:12:32Z
2025-04-24T09:12:32Z
2025-04-17
https://hdl.handle.net/10630/38473
10.1007/978-3-031-90065-5_14
As the demand for environmentally sustainable computing grows, understanding the energy consumption of AI systems has become increasingly important. This paper explores how hysteretic phenomena and implementation choices affect the energy consumption of evolutionary algorithms (EAs). Specifically, we consider the case of running EAs in batch and show how back-to-back executions can put a significant strain on the underlying processing device, resulting in increased energy consumption. An experimental analysis indicates that the introduction of short pauses can alleviate this problem and reduce consumption by up to 9% in the considered benchmark. We also conduct a comparative analysis between two twin implementations of the same EA library in Java and C++, revealing that the latter scales better in terms of energy efficiency and running time, thus underpinning the importance of implementation decisions and best practices when aiming to optimize an algorithm’s energy consumption.
eng
Algoritmos evolutivos
Tecnología limpia
Evaluating the Impact of Hysteretic Phenomena and Implementation Choices on Energy Consumption in Evolutionary Algorithms.
conference output
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URL
https://riuma.uma.es/bitstreams/ad72a804-5be0-4013-8e0c-33558d8e3a43/download
File
MD5
d4f6fb8f965aa63657e8034fc2049baa
627901
application/pdf
evostar2025.pdf