Evaluating evolution as a learning algorithm

TL;DR

This paper interprets natural selection as a learning algorithm for genotype superiority, comparing it to a Bayesian approach that uses information theory to improve learning speed at the expense of increased uncertainty fluctuations.

Contribution

It introduces a novel Bayesian learning algorithm based on a communication channel analogy, enhancing the understanding of evolution as a learning process.

Findings

The Bayesian algorithm identifies genotype superiority faster than Moran model.

The Bayesian approach exhibits larger fluctuations in uncertainty.

The study links evolutionary dynamics with information-theoretic concepts.

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.