Genetic Improvement of Programs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{langdon:2014:synasc,
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author = "William B. Langdon",
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title = "Genetic Improvement of Programs",
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booktitle = "16th International Symposium on Symbolic and Numeric
Algorithms for Scientific Computing (SYNASC 2014)",
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year = "2014",
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editor = "Franz Winkler and Viorel Negru and Tetsuo Ida and
Tudor Jebelean and Dana Petcu and Stephen Watt and
Daniela Zaharie",
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pages = "14--19",
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address = "Timisoara",
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month = "22-25 " # sep,
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organisation = "Department of Computer Science, West University of
Timisoara, Romania; + Research Institute for Symbolic
Computation, Johannes Kepler University, Linz, Austria;
+ Research Institute e-Austria, Timisoara, Romania.",
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publisher = "IEEE",
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note = "Keynote",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, GI, Automatic software re-engineering,
Bowtie2GP, multiple objective exploration, search based
software engineering (SBSE), GPGPU",
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isbn13 = "978-1-4799-8448-0",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2014_synasc.pdf",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2014_synasc_abstract.pdf",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7034660",
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DOI = "doi:10.1109/SYNASC.2014.10",
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size = "6 pages",
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abstract = "Genetic programming can optimise software, including:
evolving test benchmarks, generating hyper-heuristics
by searching meta-heuristics, generating communication
protocols, composing telephony systems and web
services, generating improved hashing and C++ heap
managers, redundant programming and even automatic bug
fixing. Particularly in embedded real-time or mobile
systems, there may be many ways to trade off expenses
(such as time, memory, energy, power consumption) vs.
functionality. Human programmers cannot try them all.
Also the best multi-objective Pareto trade off may
change with time, underlying hardware and network
connection or user behaviour. It may be GP can
automatically suggest different trade offs for each new
market. Recent results include substantial speed up by
evolving a new version of a program customised for a
special case.",
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notes = "http://synasc.ro/2014/invited-speakers/william-langdon/
CPS Also known as \cite{7034660}",
- }
Genetic Programming entries for
William B Langdon
Citations