- author = "Daniel Howard",
- title = "A Tunable Deceptive Problem to Challenge Genetic and Evolutionary Computation and Other {A.I.}",
- booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)",
- year = "2018",
- pages = "160--162",
- address = "Sydney, Australia",
- month = "3-7 " # dec,
- organisation = "Western Sydney University",
- 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",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDE.pdf",
-
URL = "
https://www.researchgate.net/publication/330474606_A_Tunable_Deceptive_Problem_to_Challenge_Genetic_and_Evolutionary_Computation_and_Other_AI",
-
DOI = "
doi:10.1109/iCMLDE.2018.00038",
- size = "3 pages",
- 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.",
-
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}",
