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Open AccessArticle

Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods

by 1,†, 2,3,*,† and 1,†
1
Department of Differential Equations, Mathematical and Numerical Analysis, Lobachevsky State University, 603950 Nizhni Novgorod, Russia
2
School of Mathematics and Actuarial Science, University of Leicester, Leicester LE1 7RH, UK
3
Institute of Ecology and Evolution, Russian Academy of Sciences, 119071 Moscow, Russia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2021, 23(1), 35; https://doi.org/10.3390/e23010035
Received: 3 November 2020 / Revised: 24 December 2020 / Accepted: 25 December 2020 / Published: 29 December 2020
Here, we propose a computational approach to explore evolutionary fitness in complex biological systems based on empirical data using artificial neural networks. The essence of our approach is the following. We first introduce a ranking order of inherited elements (behavioral strategies or/and life history traits) in considered self-reproducing systems: we use available empirical information on selective advantages of such elements. Next, we introduce evolutionary fitness, which is formally described as a certain function reflecting the introduced ranking order. Then, we approximate fitness in the space of key parameters using a Taylor expansion. To estimate the coefficients in the Taylor expansion, we utilize artificial neural networks: we construct a surface to separate the domains of superior and interior ranking of pair inherited elements in the space of parameters. Finally, we use the obtained approximation of the fitness surface to find the evolutionarily stable (optimal) strategy which maximizes fitness. As an ecologically important study case, we apply our approach to explore the evolutionarily stable diel vertical migration of zooplankton in marine and freshwater ecosystems. Using machine learning we reconstruct the fitness function of herbivorous zooplankton from empirical data and predict the daily trajectory of a dominant species in the northeastern Black Sea. View Full-Text
Keywords: zooplankton; diel vertical migration; evolutionarily stable strategy; evolutionary fitness; ranking order; machine-learned ranking; pattern recognition zooplankton; diel vertical migration; evolutionarily stable strategy; evolutionary fitness; ranking order; machine-learned ranking; pattern recognition
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MDPI and ACS Style

Kuzenkov, O.; Morozov, A.; Kuzenkova, G. Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods. Entropy 2021, 23, 35. https://doi.org/10.3390/e23010035

AMA Style

Kuzenkov O, Morozov A, Kuzenkova G. Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods. Entropy. 2021; 23(1):35. https://doi.org/10.3390/e23010035

Chicago/Turabian Style

Kuzenkov, Oleg; Morozov, Andrew; Kuzenkova, Galina. 2021. "Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods" Entropy 23, no. 1: 35. https://doi.org/10.3390/e23010035

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