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dc.contributor.authorMerelo Guervos, Juan Julián 
dc.contributor.authorMerelo-Molina, Cecilia
dc.date.accessioned2025-11-10T07:58:17Z
dc.date.available2025-11-10T07:58:17Z
dc.date.issued2025-11-09
dc.identifier.urihttps://hdl.handle.net/10481/107864
dc.description.abstractGreen computing tries to push a series of best practices that, in general, reduce the amount of energy consumed to perform a given piece of work. There are no fixed rules for {\em greening} an algorithm implementation, which means that we need to create a methodology that, after profiling the energy spent by an algorithm implementation, comes up with specific rules that will optimize the amount of energy spent. In population based algorithms, the exploration/exploitation balance is one of the most critical aspects. The algorithm we will be working with in this paper called Brave New Algorithm was designed with the main objective of keeping that balance in an optimal way through the stratification of the population. In this paper we will analyze how this balance affects the energy consumption of the algorithm.es_ES
dc.description.sponsorshipMinisterio español de Economía y Competitividad: proyecto PID2023-147409NB-C21.es_ES
dc.language.isoenges_ES
dc.rightsAtribución-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectGreen Computinges_ES
dc.subjectEnergy profilinges_ES
dc.subjectMetaheuristicses_ES
dc.titleAnalyzing how the exploration/exploitation trade off in biologically-inspired algorithms affects energy consumptiones_ES
dc.typereportes_ES
dc.rights.accessRightsopen accesses_ES
dc.type.hasVersionSMURes_ES


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Atribución-CompartirIgual 4.0 Internacional
Except where otherwise noted, this item's license is described as Atribución-CompartirIgual 4.0 Internacional