Abstract
Making evolutionary algorithms greener implies tackling implementation issues from different angles. Practitioners need to focus on those that can be more easily leveraged, such as the choice of the language that is going to be used; high-level (interpreted), mid-level (based on multi-platform virtual machines), and low-level (native) languages will need power in different ways, and choosing one or the other will have an impact on energy consumption. We will be looking at the implementation of key evolutionary algorithm functions, in three languages at three different levels: the high-level JavaScript, the mid-level Kotlin, which runs on the Java Virtual Machine, and the low-level Zig. Looking beyond the obvious, as the lower the level, the less energy consumption should be expected, we will try to have a more holistic view of the implementation of the algorithms in order to extract best practices regarding its green implementation.
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Data Availability Statement
The source of this paper as well as the data and whole writing history is available from https://github.com/JJ/energy-ga-icsoft-2023 under a GPL license.
Notes
- 1.
Described in https://www.un.org/millenniumgoals/bkgd.shtml, for instance.
- 2.
Please note that there are no languages that are considered low-level and interpreted, although there are minimalist interpreted languages like Lua [9].
- 3.
Which, for the purposes of this paper, is considered mid-level, since it compiles to bytecode.
- 4.
Please note that at the time of writing this, many other versions have been published; we have used this one to be able to compare with previous papers. These same papers show that its performance and energy consumption have important improvements, so in case of close calls, we would recommend retaking measurements at the time of running your experiments.
- 5.
We are not saying here that it is not an energy-consuming operation, even more so here where we are generating all chromosomes we are using in a single loop; however, while the initial population is generated only once in an EA, every other operation used here is going to be repeated every generation; thus, in terms of either time of energy consumption, the proportion employed by chromosome generation will be one vs. number of generations needed to find the solution.
- 6.
zig will greatly vary the amount of energy needed depending on relatively irrelevant choices such as using constant or mutable pointers; in a previous experiment run, included in the repository, that used mutable pointers, energy consumption was almost 10% higher.
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Acknowledgements
This work is supported by the Ministerio español de Economía y Competitividad (Spanish Ministry of Competitivity and Economy) under project PID2020-115570GB-C22 (DemocratAI::UGR).
We are also very grateful to the zig community, withouts which programming these operations in that language would have been impossible.
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Merelo-Guervós, J.J., García-Valdez, M. (2025). How Evolutionary Algorithms Consume Energy Depending on the Language and Its Level. In: Festa, P., Ferone, D., Pastore, T., Pisacane, O. (eds) Learning and Intelligent Optimization. LION 2024. Lecture Notes in Computer Science, vol 14990. Springer, Cham. https://doi.org/10.1007/978-3-031-75623-8_20
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