Toward a unifying framework for evolutionary processes

Tiago Paixão, Golnaz Badkobeh, Nick Barton, Doğan Çörüş, Duc Cuong Dang, Tobias Friedrich, Per Kristian Lehre, Dirk Sudholt, Andrew M. Sutton, Barbora Trubenová

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)28-43
Number of pages16
JournalJournal of Theoretical Biology
Volume383
DOIs
StatePublished - Oct 1 2015

Bibliographical note

Funding Information:
The authors would like to acknowledge Timo Kötzing for initial discussions leading to this work and to Lee Altenberg and an anonymous reviewer for very constructive comments. The research leading to these results has received funding from the European Union Seventh Framework Programme ( FP7/2007-2013 ) under grant agreement no. 618091 (SAGE) and from ERC Advanced Grant ERC-2009-AdG-250152 SELECTIONINFORMATION.

Publisher Copyright:
© 2015 The Authors.

Keywords

  • Evolution
  • Evolutionary computation
  • Mathematical modelling
  • Population genetics

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