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arXiv · 2208.00911

Evaluating evolution as a learning algorithm

Miles Miller-Dickson·Christopher Rose·C. Brandon Ogbunugafor·I. Saira Mian
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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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Miles Miller-Dickson, Christopher Rose, C. Brandon Ogbunugafor, I. Saira Mian. 2022-08-01. Evaluating evolution as a learning algorithm. https://arxiv.org/abs/2208.00911

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