Sustaining Evolution for Shallow Embodied Intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Langdon:2024:EI,

• author = "W. B. Langdon and Daniel Hulme",
• title = "Sustaining Evolution for Shallow Embodied Intelligence",
• booktitle = "Proceedings of 2024 International Conference on Embodied Intelligence, EI-2024",
• year = "2024",
• address = "Internet, Cambridge, UK",
• month = "20-22 " # mar,
• organisation = "Bio-Inspired Robotics, Cambridge",
• note = "forthcoming",
• keywords = "genetic algorithms, genetic programming, genetic improvement, Perspectives and outlooks, long term evolution experiment, information theory, entropy, software robustness, continuous improvement artificial intelligence, being shallow, shallow mangrove trees, porous software computer architectures, geometrical structures",
• size = "14 pages",
• abstract = "Lenski's experiments with E. Coli show Biology can sustain continual evolutionary improvement. However long term evolutionary experiments (LTEE) with evolutionary computing find that information theory's failed disruption propagation (FDP) in deeply nested genetic programming (GP) hierarchies can greatly slow adaptation. We propose that researchers aiming at embodied artificial intelligence should control software robustness by using porous high surface area geometrical architectures, perhaps composed of many shallow mangrove like tree structures intimately linked to their data rich fitness environment",
• notes = "

  https://embodied-intelligence.org/wp-content/uploads/2024/03/ei24-conference-programme-slides-final-2.pdf

  to be Published after the conference.",

}

Genetic Programming entries for William B Langdon Daniel Hulme

Citations