How evolution learns to generalise: : using the principles of learning theory to understand the evolution of developmental organisation
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20671236
- Type
- D - Journal article
- DOI
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10.1371/journal.pcbi.1005358
- Title of journal
- PLoS Computational Biology
- Article number
- e1005358
- First page
- 1
- Volume
- 13
- Issue
- 4
- ISSN
- 1553-734X
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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4
- Research group(s)
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-
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work shows how machine learning principles can be transferred across disciplines to better understand evolutionary processes - e.g. how parsimony pressure facilitates the evolution of evolvability (ability of evolution to produce generalised solutions that are pre-adapted to novel environments). This work formed a key sub-project within the £7.5M funding for the world’s largest project to extend evolutionary theory (Grant Number 60501, https://www.templeton.org/grant/putting-the-extended-evolutionary-synthesis-to-the-test).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -