Abstract
Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasive use of networked handheld devices and wearables. Evolutionary algorithms (EAs) are ideally suited for this kind of environments due to their intrinsic flexibility and adaptiveness, provided they operate on viable energy terms. In this work we analyze the energy requirements of EAs, and particularly one of their main flavours, genetic programming (GP), on several computational platforms and study the impact that parametrisation has on these requirements, paving the way for a future generation of energy-aware EAs. As experimentally demonstrated, handheld devices and tiny computer models mainly used for educational purposes may be the most energy efficient ones when looking for solutions by means of EAs.
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In the case of the blade, we can observe that a population with 1000 individuals consumes less energy than with 500 individuals. This phenomenon is due to the processor frequency decreases because more memory is needed.
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Acknowledgements
We acknowledge support from Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-{1,2,3}-P), from University of Granada, PROY-PP2015-06 (Plan Propio 2015 UGR), from Junta de Andalucía under project DNEMESIS (P10-TIC-6083), from Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech, from Junta de Extremadura FEDER, project GR15068 and FP7-PEOPLE-2013 IRSES Grant 612689 ACoBSEC.
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de Vega, F.F. et al. (2016). A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_51
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