Evaluating evolution as a learning algorithm

2022/08/01 by Miles Miller-Dickson, Christopher Rose, Miller-Dickson, Miles +4
Biochemistry, Genetics and Molecular Biology · Social Sciences · #Adaptation and Self-Organizing Systems (nlin.AO) #Evolution and Genetic Dynamics #Evolutionary Game Theory and Cooperation #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Populations and Evolution (q-bio.PE)

paper · pdf · doi:10.48550/arxiv.2208.00911

openalex publication_date 2022/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We interpret the Moran model of natural selection and drift as an algorithm for learning features of a simplified fitness landscape, specifically genotype superiority. This algorithm's efficiency in extracting these characteristics is evaluated by comparing it to a novel Bayesian learning algorithm developed using information-theoretic tools. This algorithm makes use of a communication channel analogy between an environment and an evolving population. We use the associated channel-rate to determine an informative population-sampling procedure. We find that the algorithm can identify genotype superiority faster than the Moran model but at the cost of larger fluctuations in uncertainty.

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