- author = "Mahmoud A. Bokhari and Markus Wagner and Brad Alexander",
- title = "Genetic Improvement of Software Efficiency: The Curse of Fitness Estimation",
- booktitle = "9th edition of GI @ GECCO 2020",
- year = "2020",
- month = jul # " 8-12",
- editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward",
- publisher = "ACM",
- address = "Internet",
- pages = "1926--1927",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Machine Learning, Non-Functional Properties, batteries, Energy Consumption, Mobile Applications, Android",
- isbn13 = "978-1-4503-7127-8",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp145s2-file1.pdf",
-
DOI = "
doi:10.1145/3377929.3398109",
- size = "2 pages",
- abstract = "Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straight-forward task. we illustrate some of the challenges incomputing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.",
-
notes = "nexus6 android7 smartphone, garbage collection,
surrogate fitness
function.
https://gi-gecco-20.gi-workshops.org/
Also known as \cite{Bokhari:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398109} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
