Population size influence on the energy consumption of genetic programming Díaz Álvarez, Josefa Castillo Valdivieso, Pedro Ángel Energy consumption Evolutionary algorithms Energy-aware computing Performance measurements The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness under project TIN2017-85727-C4-\{2,4\}-P. Grant PID2020-115570GB-C22 and PID2020-115570GB-C21 funded by MCIN/AEI/10.13039/501100011033. Junta de Extremadura under project GR15068. 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 routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. 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. 2022-05-12T12:21:04Z 2022-05-12T12:21:04Z 2022-04-28 info:eu-repo/semantics/article Díaz-Álvarez, J... [et al.]. (2022). Population size influence on the energy consumption of genetic programming. Measurement and Control. [https://doi.org/10.1177/00202940211064471] http://hdl.handle.net/10481/74815 10.1177/00202940211064471 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España SAGE