Recombination Is Surprisingly Constructive for Artificial Gene Regulatory Networks in the Context of Selection for Developmental Stability

Yifei Wang, Yinghong Lan, Daniel Weinreich, Nicholas Priest, Joanna Bryson

Research output: Contribution to conferencePaperpeer-review


Recombination is ubiquitous in multicellular plants, animals and even fungi. Many studies have shown that recombination can generate a great amount of genetic innovations, but it is also believed to damage well-adapted lineages, causing debates over how organisms cope with such disruptions. Using an established model of artificial gene regulatory networks, here we show that recombination may not be as destructive as expected. Provided only that there is selection for developmental stability, recombination can establish and maintain lineages with reliably better phenotypes compared to asexual reproduction. Contrary to expectation, this does not appear to be a simple side effect of higher levels of variation. A simple model of the underlying dynamics demonstrates a surprisingly high robustness in these lineages against the disruption caused by recombination. Contrary to expectation, lineages subject to recombination are less likely to produce offspring suffering truncation selection for instability than asexual lineages subject to simple mutation. These findings indicate the fundamental differences between recombination and high mutation rates, which has important implications for understanding both biological innovation and hierarchically structured models of machine learning.
Original languageEnglish
Number of pages537
Publication statusPublished - Jul 2015
EventThe 13th European Conference on Artificial Life - York, UK United Kingdom
Duration: 20 Jul 201524 Jul 2015


ConferenceThe 13th European Conference on Artificial Life
Country/TerritoryUK United Kingdom


  • Evolution Dynamics
  • Recombination
  • Gene Regulatory Networks
  • Developmental Stability
  • Evolution of Sex
  • Maintenance of Sexual Reproduction
  • Genetic Innovations
  • Evolvability
  • Hierarchical Network Structure
  • Machine Learning


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