How intelligence can change the course of evolution

Code

• github.com/leaguilar/baldwin_veering
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• github.com/bennati/baldwin_veering
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Abstract

The effect of phenotypic plasticity on evolution, the so-called Baldwin effect, has been studied extensively for more than 100 years. Plasticity is known to influence the speed of evolution towards a specific genetic configuration, but whether it also influences what that genetic configuration is, is still an open question. This question is investigated, in an environment where the distribution of resources follows seasonal cycles, both analytically and experimentally by means of an agent-based model of a foraging task. Individuals can either specialize to foraging only one specific resource type or generalize to foraging all resource types at a low success rate. It is found that the introduction of learning, one instance of phenotypic plasticity, changes what genetic configuration evolves. Specifically, the genome of learning agents evolves a predisposition to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would evolve a predisposition to maximize the foraging efficiency for a specific resource type. This paper expands the literature at the interface between Biology and Machine Learning by identifying the Baldwin effects in cyclically-changing environments and demonstrating that learning can change the outcome of evolution.

Tasks

Reproductions