Por uma estrutura unificadora de processos evolutivos

sexta-feira, janeiro 15, 2016

Journal of Theoretical Biology

Volume 383, 21 October 2015, Pages 28–43

Toward a unifying framework for evolutionary processes

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

a Institute of Science and Technology, Am Campus 1, A3400 Klosterneuburg, Austria

b University of Nottingham, UK

c University of Sheffield, UK

d Hasso Plattner Institute, Potsdam, Germany

Received 27 November 2014, Revised 8 July 2015, Accepted 15 July 2015, Available online 26 July 2015

Open Access funded by European Research CouncilUnder a Creative Commons license


• A unifying framework for evolutionary processes. 

• Formalizing the defining properties of the different kinds of processes: 

○ Variation operators (mutation and recombination). 
○ Selection operators. 

• Formalizing several common examples of these operators in terms of our framework. 

• Proving that these common operators respect the properties that we define for their class. 

• Casting several classical models and algorithms from both fields into our framework. 


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. 

Keywords Population genetics; Evolution; Evolutionary computation; Mathematical modelling