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dc.contributor.authorDíaz Álvarez, Josefa
dc.contributor.authorCastillo Valdivieso, Pedro Ángel
dc.identifier.citationDíaz-Álvarez, J... [et al.]. (2022). Population size influence on the energy consumption of genetic programming. Measurement and Control. []es_ES
dc.descriptionThe 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.es_ES
dc.description.abstractEvolutionary 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.es_ES
dc.description.sponsorshipSpanish Government TIN2017-85727-C4-\{2,4\}-P PID2020-115570GB-C22 PID2020-115570GB-C21 MCIN/AEI/10.13039/501100011033es_ES
dc.description.sponsorshipJunta de Extremadura GR15068es_ES
dc.rightsAtribución 3.0 España*
dc.subjectEnergy consumption es_ES
dc.subjectEvolutionary algorithmses_ES
dc.subjectEnergy-aware computinges_ES
dc.subjectPerformance measurementses_ES
dc.titlePopulation size influence on the energy consumption of genetic programminges_ES

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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España