Evomorph: Morphological Modularization in A.I. for Machine Vision Inspired by Embryology
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Howard:2018:iCMLDEc,
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author = "Daniel Howard",
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title = "Evomorph: Morphological Modularization in {A.I.} for
Machine Vision Inspired by Embryology",
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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 = "163--166",
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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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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, embryology,
modularization, machine vision, image analysis, object
detection, classification, template matching, pattern
matching, Artificial Intelligence, Evolutionary
Computation, code re-use",
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isbn13 = "978-1-7281-0405-8",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEc.pdf",
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URL = "https://www.researchgate.net/publication/330472647_Explainable_AI_The_Promise_of_Genetic_Programming_Multi-run_Subtree_Encapsulation",
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DOI = "doi:10.1109/iCMLDE.2018.00039",
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size = "4 pages",
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abstract = "Nature likely implements modularization in
multicellular developmental biology using the chemical
context of the cell, cell division generational
distance, and genetic triggers. Inspired in this,
Evomorph is a proposed heuristic method of Artificial
Intelligence that pairs these concepts with
Evolutionary Computation. It is described here as a
flexible template matching for object detection in
Machine Vision.",
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notes = "Also known as \cite{8614022}
gone 2023
http://www.icmlde.net.au/IndustrialTrack.aspx
Howard Science Ltd. Malvern, UK.",
- }
Genetic Programming entries for
Daniel Howard
Citations