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Toward a unifying framework for evolutionary processes
The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutio...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572021/ https://www.ncbi.nlm.nih.gov/pubmed/26215686 http://dx.doi.org/10.1016/j.jtbi.2015.07.011 |
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author | Paixão, Tiago Badkobeh, Golnaz Barton, Nick Çörüş, Doğan Dang, Duc-Cuong Friedrich, Tobias Lehre, Per Kristian Sudholt, Dirk Sutton, Andrew M. Trubenová, Barbora |
author_facet | Paixão, Tiago Badkobeh, Golnaz Barton, Nick Çörüş, Doğan Dang, Duc-Cuong Friedrich, Tobias Lehre, Per Kristian Sudholt, Dirk Sutton, Andrew M. Trubenová, Barbora |
author_sort | Paixão, Tiago |
collection | PubMed |
description | The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. |
format | Online Article Text |
id | pubmed-4572021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-45720212015-10-21 Toward a unifying framework for evolutionary processes Paixão, Tiago Badkobeh, Golnaz Barton, Nick Çörüş, Doğan Dang, Duc-Cuong Friedrich, Tobias Lehre, Per Kristian Sudholt, Dirk Sutton, Andrew M. Trubenová, Barbora J Theor Biol Article The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. Elsevier 2015-10-21 /pmc/articles/PMC4572021/ /pubmed/26215686 http://dx.doi.org/10.1016/j.jtbi.2015.07.011 Text en © 2015 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Paixão, Tiago Badkobeh, Golnaz Barton, Nick Çörüş, Doğan Dang, Duc-Cuong Friedrich, Tobias Lehre, Per Kristian Sudholt, Dirk Sutton, Andrew M. Trubenová, Barbora Toward a unifying framework for evolutionary processes |
title | Toward a unifying framework for evolutionary processes |
title_full | Toward a unifying framework for evolutionary processes |
title_fullStr | Toward a unifying framework for evolutionary processes |
title_full_unstemmed | Toward a unifying framework for evolutionary processes |
title_short | Toward a unifying framework for evolutionary processes |
title_sort | toward a unifying framework for evolutionary processes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572021/ https://www.ncbi.nlm.nih.gov/pubmed/26215686 http://dx.doi.org/10.1016/j.jtbi.2015.07.011 |
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