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Published March 2004 | Published
Journal Article Open

Digital evolution in time-dependent fitness landscapes

Abstract

We study the response of populations of digital organisms that adapt to a time-varying (periodic) fitness landscape of two oscillating peaks. We corroborate in general predictions from quasi-species theory in dynamic landscapes, such as adaptation to the average fitness landscape at small periods (high frequency) and quasistatic adaptation at large periods (low frequency). We also observe adaptive phase shifts (time tags between a change in the fitness landscape and art adaptive change in the population) that indicate a low-pass filter effect, in agreement with existing theory,. Finally, we witness long-term adaptation to fluctuating environments not anticipated in previous theoretical work.

Additional Information

© 2004 Massachusetts Institute of Technology. Posted Online March 11, 2006. We would like to thank Allan Drummond for technical assistance, and Chris Adami, Santiago Elena, and Isabel Novella for helpful comments and suggestions. C.O.W. was supported by NSF grant DEB-9981397 to Chris Adami. All data were obtained on a cluster of 160 processors at Michigan State University, supported by the NSF under the aforementioned contract.

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August 22, 2023
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