The influence of learning on evolution: a mathematical framework.

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Version: Author's accepted manuscript
Serval ID
serval:BIB_654B871003F7
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
The influence of learning on evolution: a mathematical framework.
Journal
Artificial Life
Author(s)
Paenke I., Kawecki T.J., Sendhoff B.
ISSN
1064-5462
Publication state
Published
Issued date
2009
Peer-reviewed
Oui
Volume
15
Number
2
Pages
227-245
Language
english
Abstract
The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.
Keywords
learning, evolution, Baldwin effect, model, theory, computation, AI
Pubmed
Web of science
Create date
31/03/2009 10:24
Last modification date
20/08/2019 14:21
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