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Selecting miners within blockchain-based systems using evolutionary algorithms for energy optimisation.

, , , and . GECCO Companion, page 291-292. ACM, (2021)

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Improving Test Coverage of Formal Verification Systems via Beam Search., and . GECCO (Companion), page 1357-1358. ACM, (2015)A Hybrid Distributed EA Approach for Energy Optimisation on Smartphones., and . Empir. Softw. Eng., 27 (6): 155 (2022)Genetic improvement of software efficiency: the curse of fitness estimation., , and . GECCO Companion, page 1926-1927. ACM, (2020)Towards rigorous validation of energy optimisation experiments., , and . GECCO, page 1232-1240. ACM, (2020)Selecting miners within blockchain-based systems using evolutionary algorithms for energy optimisation., , , and . GECCO Companion, page 291-292. ACM, (2021)Deep parameter optimisation on Android smartphones for energy minimisation: a tale of woe and a proof-of-concept., , , and . GECCO (Companion), page 1501-1508. ACM, (2017)Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem Formulation., , , and . IEEE Trans. Sustain. Comput., 7 (4): 910-922 (2022)In-vivo and offline optimisation of energy use in the presence of small energy signals: A case study on a popular Android library., , and . MobiQuitous, page 207-215. ACM, (2018)Mind the gap - a distributed framework for enabling energy optimisation on modern smart-phones in the presence of noise, drift, and statistical insignificance., , , and . CEC, page 1330-1337. IEEE, (2019)Optimising Energy Consumption Heuristically on Android Mobile Phones., and . GECCO (Companion), page 1139-1140. ACM, (2016)