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.
Original language | English |
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Title of host publication | ALIFE 2020: The 2020 Conference on Artificial Life |
Publisher | MIT Press Journals |
Pages | 292-299 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 1 Jul 2020 |
Event | 2020 Conference on Artificial Life - Duration: 13 Jul 2020 → 18 Jul 2020 https://www.robot100.cz/alife2021/ |
Conference
Conference | 2020 Conference on Artificial Life |
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Abbreviated title | ALIFE 2020 |
Period | 13/07/20 → 18/07/20 |
Internet address |
Bibliographical note
© 2020 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licenseKeywords
- Coloured Noise
- Environmental Variability
- Artificial Evolutionary Systems
- Environmental Time Series