A Tunable Deceptive Problem to Challenge Genetic and Evolutionary Computation and Other A.I.
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Howard:2018:iCMLDE,
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author = "Daniel Howard",
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title = "A Tunable Deceptive Problem to Challenge Genetic and
Evolutionary Computation and Other {A.I.}",
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booktitle = "2018 International Conference on Machine Learning and
Data Engineering (iCMLDE)",
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year = "2018",
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pages = "160--162",
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address = "Sydney, Australia",
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month = "3-7 " # dec,
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organisation = "Western Sydney University",
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keywords = "genetic algorithms, genetic programming, attribute
grammar genetic programming, benchmark problem,
deceptive problem, Evolutionary Computation, AI,
solution landscape, heuristic method, tunable problem,
analytical solution, toy problem",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDE.pdf",
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URL = "https://www.researchgate.net/publication/330474606_A_Tunable_Deceptive_Problem_to_Challenge_Genetic_and_Evolutionary_Computation_and_Other_AI",
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DOI = "doi:10.1109/iCMLDE.2018.00038",
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size = "3 pages",
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abstract = "A deceptive problem with known analytical solution is
introduced. Arguably its solution search landscape is
such that heuristic methods will find it difficult to
search for the solution. The problem is tunable
offering a test bed by which to examine the performance
of different methods of heuristic and evolutionary
search.",
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notes = "p160 'A sequential computer program consisting of a
set of instructions with some inter-dependencies
between instructions is to be run on a parallel
computer. No instruction or underlying algorithm is
modified but the instructions must be distributed
optimally among a potentially unlimited number of
parallel processors, respecting the dependencies, such
that the program is run in minimum time, essentially
carries out the same computation and outputs the
similar results as its sequential version.'
Cites
\cite{conf/ichit/HowardC12}
http://www.icmlde.net.au/IndustrialTrack.aspx also
known as \cite{8614021}",
- }
Genetic Programming entries for
Daniel Howard
Citations