A biological perspective on evolutionary computation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{miikkulainen:2021:nmi,
-
author = "Risto Miikkulainen and Stephanie Forrest",
-
title = "A biological perspective on evolutionary computation",
-
journal = "Nature machine intelligence",
-
year = "2021",
-
volume = "3",
-
pages = "9--15",
-
month = "18 " # jan,
-
keywords = "genetic algorithms, genetic programming, Computational
science, Evolutionary theory",
-
ISSN = "2522-5839",
-
URL = "https://rdcu.be/clFHY",
-
URL = "https://www.nature.com/articles/s42256-020-00278-8.pdf",
-
DOI = "doi:10.1038/s42256-020-00278-8",
-
size = "7 pages",
-
abstract = "Evolutionary computation is inspired by the mechanisms
of biological evolution. With algorithmic improvements
and increasing computing resources, evolutionary
computation has discovered creative and innovative
solutions to challenging practical problems. This paper
evaluates how today's evolutionary computation compares
to biological evolution and how it may fall short. A
small number of well-accepted characteristics of
biological evolution are considered: openendedness,
major transitions in organisational structure,
neutrality and genetic drift, multiobjectivity, complex
genotype-to-phenotype mappings and co-evolution.
Evolutionary computation exhibits many of these to some
extent but more can be achieved by scaling up with
available computing and by emulating biology more
carefully. In particular, evolutionary computation
diverges from biological evolution in three key
respects: it is based on small populations and strong
selection; it typically uses direct genotype to
phenotype mappings; and it does not achieve major
organizational transitions. These shortcomings suggest
a roadmap for future evolutionary computation research,
and point to gaps in our understanding of how biology
discovers major transitions. Advances in these areas
can lead to evolutionary computation that approaches
the complexity and flexibility of biology, and can
serve as an executable model of biological processes.",
-
notes = "Perspectives
Mentioned in Forrest GI @ ICSE 2021 keynote
https://www.youtube.com/watch?v=ckM3PXs6hK8&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD
broken Aug 2021 http://www.nature.com/natmachintel",
- }
Genetic Programming entries for
Risto Miikkulainen
Stephanie Forrest
Citations