Genetic Improvement of Software Efficiency: The Curse of Fitness Estimation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Bokhari:2020:GI9,
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author = "Mahmoud A. Bokhari and Markus Wagner and
Brad Alexander",
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title = "Genetic Improvement of Software Efficiency: The Curse
of Fitness Estimation",
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booktitle = "9th edition of GI @ GECCO 2020",
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year = "2020",
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month = jul # " 8-12",
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editor = "Brad Alexander and Alexander (Sandy) Brownlee and
Saemundur O. Haraldsson and Markus Wagner and
John R. Woodward",
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publisher = "ACM",
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address = "Internet",
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pages = "1926--1927",
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, SBSE, Machine Learning, Non-Functional
Properties, batteries, Energy Consumption, Mobile
Applications, Android",
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isbn13 = "978-1-4503-7127-8",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp145s2-file1.pdf",
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DOI = "doi:10.1145/3377929.3398109",
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size = "2 pages",
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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.",
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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)",
- }
Genetic Programming entries for
Mahmoud A Bokhari
Markus Wagner
Brad Alexander
Citations