On Using Relative Information to Estimate Traits in a Darwinian Evolution Population Dynamics

Autor: Eddy Kwessi
Jazyk: angličtina
Rok vydání: 2024
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Zdroj: Axioms, Vol 13, Iss 6, p 406 (2024)
Druh dokumentu: article
ISSN: 13060406
2075-1680
DOI: 10.3390/axioms13060406
Popis: Since its inception, evolution theory has garnered much attention from the scientific community for a good reason: it theorizes how various living organisms came to be and what changes are to be expected in a certain environment. While many models of evolution have been proposed to track changes in species’ traits, not much has been said about how to calculate or estimate these traits. In this paper, using information theory, we propose an estimation method for trait parameters in a Darwinian evolution model for species with one or multiple traits. We propose estimating parameters by minimizing the relative information in a Darwinian evolution population model using either a classical gradient ascent or a stochastic gradient ascent. The proposed procedure is shown to be possible in a supervised or unsupervised learning environment, similarly to what occurs with Boltzmann machines. Simulations are provided to illustrate the method.
Databáze: Directory of Open Access Journals
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