Explainable A.I.: The Promise of Genetic Programming Multi-run Subtree Encapsulation
Created by W.Langdon from
gp-bibliography.bib Revision:1.5693
- @InProceedings{Howard:2018:iCMLDEb,
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author = "Daniel Howard and Mark A. Edwards",
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title = "Explainable {A.I.}: The Promise of Genetic Programming
Multi-run Subtree Encapsulation",
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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 = "158--159",
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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, Explainable
Artificial Intelligence, AI, Evolutionary Computation,
modularization, Subtree Encapsulation, Automatically
Defined Functions, ADF, Software Evolution, white box,
black box, expression simplification, Deep Learning,
Artificial Neural Networks, Multirun Subtree
Encapsulation, subtree database",
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URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEb.pdf",
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URL = "
https://www.researchgate.net/publication/330475457_Evomorph_Morphological_Modularization_in_AI_for_Machine_Vision_Inspired_by_Embryology",
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DOI = "
doi:10.1109/iCMLDE.2018.00037",
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size = "2 pages",
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abstract = "Deep Learning and other Artificial Neural Network
based solutions are rarely transparent, and white-box
solutions are often called for. This paper explains how
Multirun Subtree Encapsulation can provide equivalent
white box solutions to facilitate Explainable
Artificial Intelligence.",
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notes = "Howard Science Ltd, Malvern, UK
http://www.icmlde.net.au/IndustrialTrack.aspx Also
known as \cite{8614020}",
- }
Genetic Programming entries for
Daniel Howard
Mark A Edwards
Citations