Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems.

Abstract

Ecological, environmental and geophysical time series consistently exhibit the characteristics of coloured (1/ƒβ) noise. Here we briefly survey the literature on coloured noise, population persistence and related evolutionary dynamics, before introducing coloured noise as an appropriate model for environmental variation in artificial evolutionary systems. To illustrate and explore the effects of different noise colours, a simple evolutionary model that examines the trade-off between specialism and generalism in fluctuating environments is applied. The results of the model clearly demonstrate a need for greater generalism as environmental variability becomes ‘whiter’, whilst specialisation is favoured as environmental variability becomes ‘redder’. Pink noise, sitting midway between white and red noise, is shown to be the point at which the pressures for generalism and specialism balance, providing some insight in to why ‘pinker’ noise is increasingly being seen as an appropriate model of typical environmental variability. We go on to discuss how the results presented here feed in to a wider discussion on evolutionary responses to fluctuating environments. Ultimately we argue that Artificial Life as a field should embrace the use of coloured noise to produce models of environmental variability.

Publication DOI: https://doi.org/10.1162/isal_a_00284
Additional Information: © 2020 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
Event Title: 2020 Conference on Artificial Life
Event Type: Other
Event Dates: 2020-07-13 - 2020-07-18
Uncontrolled Keywords: Coloured Noise,Environmental Variability,Artificial Evolutionary Systems,Environmental Time Series
Last Modified: 27 Dec 2023 10:11
Date Deposited: 17 Jan 2022 11:19
Full Text Link:
Related URLs: https://direct. ... 20/32/292/98453 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2020-07-01
Authors: Grove, Matt
Borg, James (ORCID Profile 0000-0002-6662-0849)
Polack, Fiona

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