Created by W.Langdon from gp-bibliography.bib Revision:1.8010
we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy efficiency in noisy and fragmented eco-systems.
First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments.
Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite.
Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment.
Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.",
Genetic Programming entries for Mahmoud A Bokhari