- author = "Josefa Diaz-Alvarez and Maribel G. Arenas and Pedro A. Castillo and Francisco {Fernandez de Vega} and Francisco Chavez and Jorge Alvarado",
- title = "Population size influence on the energy consumption of Genetic Programming",
- booktitle = "Evostar 2023 Late breaking abstracts",
- year = "2023",
- editor = "Antonio M. Mora and Anna I. Esparcia-Alcazar",
- pages = "26--29",
- address = "Brno",
- month = "12-14 " # apr,
- organisation = "Species",
- keywords = "genetic algorithms, genetic programming, ECJ, Energy consumption, evolutionary algorithms, energy-aware computing, performance measurements, LID, ORDER Tree, Yokogawa WT310E, APPPowerMeter",
-
URL = "
https://arxiv.org/abs/2403.13950",
- size = "4 pages",
-
abstract = "Evolutionary Algorithms (EAs) are routinely applied to
solve a large set of optimization problems.
Traditionally, their performance in solving those
problems is analyzed using the fitness quality and
computing time, and the effect of evolutionary
operators on both metrics is frequently analyzed to
compare different versions of EAs. Nevertheless,
scientists face nowadays the challenge of considering
energy efficiency in addition to computational time,
which requires studying the algorithms energy
consumption.
This paper discusses the interest in introducing power consumption as a new metric to analyze the performance of genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different methods to measuring the power consumption have been tested.
Analyzing the population size, the results demonstrate its influence on the energy consumed: a non-linear relationship was found between the size and energy required to complete an experiment. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution.
Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.",
- notes = "(Raspberry pi, laptop, and PC",
