Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Neural and Evolutionary Computing

arXiv:1512.02100 (cs)
[Submitted on 7 Dec 2015]

Title:Digital Genesis: Computers, Evolution and Artificial Life

Authors:Tim Taylor, Alan Dorin, Kevin Korb
View PDF
Abstract:The application of evolution in the digital realm, with the goal of creating artificial intelligence and artificial life, has a history as long as that of the digital computer itself. We illustrate the intertwined history of these ideas, starting with the early theoretical work of John von Neumann and the pioneering experimental work of Nils Aall Barricelli. We argue that evolutionary thinking and artificial life will continue to play an integral role in the future development of the digital world.
Comments: Extended abstract of talk presented at the 7th Munich-Sydney-Tilburg Philosophy of Science Conference: Evolutionary Thinking, University of Sydney, 20-22 March 2014. Presentation slides from talk available at this http URL
Subjects: Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1512.02100 [cs.NE]
  (or arXiv:1512.02100v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.1512.02100

Submission history

From: Tim Taylor [view email]
[v1] Mon, 7 Dec 2015 15:53:48 UTC (9 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.NE
< prev   |   next >
new | recent | 2015-12
Change to browse by:
cs

References & Citations

1 blog link

(what is this?)

DBLP - CS Bibliography

listing | bibtex
Tim Taylor
Alan Dorin
Kevin B. Korb
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)