Reducing Energy Consumption Using Genetic Improvement

Abstract

Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software's nonfunctional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program's execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25%.

Mobile app and app store analysis, testing and optimisation

“…The keynote will conclude with some recent results from our work on genetic improvement for energy optimisation [4], deep parameter exposition [27], dreaming smart phones [8] and automated software transplantation [2,12,14], explaining how these techniques can be used for mobile app optimisation.…”

Automated Transplantation of Call Graph and Layout Features into Kate

“…Our work is closely related to recent achievements in genetic improvement, which have been able to dramatically speed up real world systems [9,14,15], port between languages [8], balance memory consumption and execution time [16], reduced energy consumption [3,13] and fix bugs [10]. Most closely related to our approach is work on auto-specialisation using transplantation [11] and grow and graft genetic improvement [6].…”

Exploring Fitness and Edit Distance of Mutated Python Programs

“…Langdon has also reported 100 fold speed-up of another DNA sequencing tool BarraCUDA [17,19,[21][22][23] and the GI improvements have now been included in the official release. Langdon's GI implementation has furthermore been used by others for specializing and optimizing the execution time of MiniSAT [30], a boolean satisfiability solver and for optimizing power consumption of that same solver [5,6]. Many others have applied or suggested GI for improving non-functional properties such as execution time [9,10,35,42], energy consumption [7,8,13,40,43] and memory usage [32,44].…”