Computer science owes a huge debt to biological
systems. The field itself came about largely as an
attempt to understand and replicate the function and
abilities of the brain, a complex biological creation.
From this early lineage has sprung many subfields
derived largely from biological metaphors: computer
vision, neural networks, evolutionary computation,
robotics, multi-agent studies, and much of artificial
intelligence. In some areas, the computer has bested
its biological counterparts in efficiency and
simplicity. But for many domains, even after decades of
hard work, the biological "real thing" is still
superior to the artificial algorithms inspired by it.
%0 Journal Article
%1 luke:1998:firsttime
%A Luke, Sean
%A Hamahashi, Shugo
%A Kyoda, Koji
%A Ueda, Hiroki
%D 1998
%J IEEE Intelligent Systems
%K DNA algorithms, biological genetic modelling, programming,
%N 5
%T Biology: See It Again -- for the First Time
%U http://www.cs.gmu.edu/~sean/papers/biology.ps.gz
%V 13
%X Computer science owes a huge debt to biological
systems. The field itself came about largely as an
attempt to understand and replicate the function and
abilities of the brain, a complex biological creation.
From this early lineage has sprung many subfields
derived largely from biological metaphors: computer
vision, neural networks, evolutionary computation,
robotics, multi-agent studies, and much of artificial
intelligence. In some areas, the computer has bested
its biological counterparts in efficiency and
simplicity. But for many domains, even after decades of
hard work, the biological "real thing" is still
superior to the artificial algorithms inspired by it.
@article{luke:1998:firsttime,
abstract = {Computer science owes a huge debt to biological
systems. The field itself came about largely as an
attempt to understand and replicate the function and
abilities of the brain, a complex biological creation.
From this early lineage has sprung many subfields
derived largely from biological metaphors: computer
vision, neural networks, evolutionary computation,
robotics, multi-agent studies, and much of artificial
intelligence. In some areas, the computer has bested
its biological counterparts in efficiency and
simplicity. But for many domains, even after decades of
hard work, the biological {"}real thing{"} is still
superior to the artificial algorithms inspired by it.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Luke, Sean and Hamahashi, Shugo and Kyoda, Koji and Ueda, Hiroki},
biburl = {https://www.bibsonomy.org/bibtex/296680558f5bdb043f7144ce04afb8d25/brazovayeye},
interhash = {58af5b54066d8d4085dd799dd634bd6a},
intrahash = {96680558f5bdb043f7144ce04afb8d25},
journal = {IEEE Intelligent Systems},
keywords = {DNA algorithms, biological genetic modelling, programming,},
month = {September/October},
notes = {Invited Article. Argues for a revisitation of the
biological roots behind artificial intelligence and
evolutionary computation},
number = 5,
size = {3 pages},
timestamp = {2008-06-19T17:45:57.000+0200},
title = {Biology: See It Again -- for the First Time},
url = {http://www.cs.gmu.edu/~sean/papers/biology.ps.gz},
volume = 13,
year = 1998
}