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How evolution learns to generalise:: using the principles of learning theory to understand the evolution of developmental organisation

How evolution learns to generalise:: using the principles of learning theory to understand the evolution of developmental organisation
How evolution learns to generalise:: using the principles of learning theory to understand the evolution of developmental organisation
One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.
1553-734X
1-20
Kouvaris, Kostas
ced6ce72-5863-4af8-82f6-dea1576b9d23
Clune, Jeff
2a9284aa-86d9-4279-aefe-c938cf31ad4d
Kounios, Loizos
9bcd10ab-7b19-4236-953f-9fa1a9a726b1
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Kouvaris, Kostas
ced6ce72-5863-4af8-82f6-dea1576b9d23
Clune, Jeff
2a9284aa-86d9-4279-aefe-c938cf31ad4d
Kounios, Loizos
9bcd10ab-7b19-4236-953f-9fa1a9a726b1
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75

Kouvaris, Kostas, Clune, Jeff, Kounios, Loizos, Brede, Markus and Watson, Richard A. (2017) How evolution learns to generalise:: using the principles of learning theory to understand the evolution of developmental organisation. PLoS Computational Biology, 13 (4), 1-20, [e1005358]. (doi:10.1371/journal.pcbi.1005358).

Record type: Article

Abstract

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

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More information

Accepted/In Press date: 5 January 2017
e-pub ahead of print date: 6 April 2017
Published date: 6 April 2017
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 404420
URI: http://eprints.soton.ac.uk/id/eprint/404420
ISSN: 1553-734X
PURE UUID: 2c4514ff-1142-490c-9023-4663f8d1b39f

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Date deposited: 09 Jan 2017 11:16
Last modified: 07 Oct 2020 05:48

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Contributors

Author: Kostas Kouvaris
Author: Jeff Clune
Author: Loizos Kounios
Author: Markus Brede
Author: Richard A. Watson

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