↓ Skip to main content

PLOS

A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms

Overview of attention for article published in PLOS ONE, April 2013
Altmetric Badge

Mentioned by

blogs
1 blog
twitter
3 X users

Readers on

mendeley
14 Mendeley
Title
A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0060466
Pubmed ID
Authors

Laura M. Grabowski, David M. Bryson, Fred C. Dyer, Robert T. Pennock, Charles Ofria

Abstract

Investigating the evolution of animal behavior is difficult. The fossil record leaves few clues that would allow us to recapitulate the path that evolution took to build a complex behavior, and the large population sizes and long time scales required prevent us from re-evolving such behaviors in a laboratory setting. We present results of a study in which digital organisms-self-replicating computer programs that are subject to mutations and selection-evolved in different environments that required information about past experience for fitness-enhancing behavioral decisions. One population evolved a mechanism for step-counting, a surprisingly complex odometric behavior that was only indirectly related to enhancing fitness. We examine in detail the operation of the evolved mechanism and the evolutionary transitions that produced this striking example of a complex behavior.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 14%
United States 1 7%
Germany 1 7%
Unknown 10 71%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 21%
Professor > Associate Professor 3 21%
Student > Ph. D. Student 3 21%
Researcher 3 21%
Student > Doctoral Student 1 7%
Other 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 36%
Computer Science 3 21%
Immunology and Microbiology 1 7%
Psychology 1 7%
Physics and Astronomy 1 7%
Other 3 21%