- author = "Daniel Howard",
- title = "Evomorph: Morphological Modularization in {A.I.} for Machine Vision Inspired by Embryology",
- booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)",
- year = "2018",
- pages = "163--166",
- address = "Sydney, Australia",
- month = "3-7 " # dec,
- organisation = "Western Sydney University",
- publisher = "IEEE",
- 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",
- isbn13 = "978-1-7281-0405-8",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEc.pdf",
-
URL = "
https://www.researchgate.net/publication/330472647_Explainable_AI_The_Promise_of_Genetic_Programming_Multi-run_Subtree_Encapsulation",
-
DOI = "
doi:10.1109/iCMLDE.2018.00039",
- size = "4 pages",
- 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.",
-
notes = "Also known as \cite{8614022}
gone 2023 http://www.icmlde.net.au/IndustrialTrack.aspx
Howard Science Ltd. Malvern, UK.",
