Evolvability and complexity properties of the digital circuit genotype-phenotype map

Recent research on genotype-phenotype (G-P) maps in natural evolution has contributed significantly to our understanding of neutrality, redundancy, robustness, and evolvability. Here we investigate the properties of the digital logic gate G-P map and show that this map shares many of the common properties of natural G-P maps, with the exception of a positive relationship between evolvability and robustness. Our results show that in some cases robustness and evolvability may be negatively related as a result of the method used to approximate evolvability. We give two definitions of circuit complexity and empirically show that these two definitions are closely related. This study leads to a better understanding of the relationships between redundancy, robustness, and evolvability in genotype-phenotype maps. We further investigate these results in the context of complexity and show the relationships between phenotypic complexity and phenotypic redundancy, robustness and evolvability.

[1]  Sergey Gavrilets,et al.  PERSPECTIVE: MODELS OF SPECIATION: WHAT HAVE WE LEARNED IN 40 YEARS? , 2003, Evolution; international journal of organic evolution.

[2]  A. Wagner Information theory, evolutionary innovations and evolvability , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[3]  C. Adami,et al.  Evolution of Biological Complexity , 2000, Proc. Natl. Acad. Sci. USA.

[4]  L. Altenberg,et al.  PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY , 1996, Evolution; international journal of organic evolution.

[5]  Kevin B. Korb,et al.  Evolution unbound: releasing the arrow of complexity , 2011 .

[6]  Karthik Raman,et al.  The evolvability of programmable hardware , 2010, Journal of The Royal Society Interface.

[7]  J. Crutchfield,et al.  The Evolutionary Unfolding of Complexity , 1999, adap-org/9903001.

[8]  A. Wagner Robustness and evolvability: a paradox resolved , 2008, Proceedings of the Royal Society B: Biological Sciences.

[9]  J. Gravner,et al.  Percolation on the fitness hypercube and the evolution of reproductive isolation. , 1997, Journal of theoretical biology.

[10]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[11]  Marcus Hutter,et al.  Algorithmic Information Theory , 1977, IBM J. Res. Dev..

[12]  A. Wagner The role of robustness in phenotypic adaptation and innovation , 2012, Proceedings of the Royal Society B: Biological Sciences.

[13]  S. Ahnert,et al.  The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps , 2015, Journal of The Royal Society Interface.

[14]  Andreas Wagner,et al.  Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks , 2009, PLoS Comput. Biol..

[15]  Yuanzhu Chen,et al.  Measuring evolvability and accessibility using the hyperlink-induced topic search algorithm , 2018, GECCO.

[16]  Lionel Barnett,et al.  Netcrawling-optimal evolutionary search with neutral networks , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[17]  G. Fusco,et al.  Effects of Phenotypic Robustness on Adaptive Evolutionary Dynamics , 2019, bioRxiv.

[18]  Evandro Agazzi,et al.  What is Complexity , 2002 .

[19]  Ricard V Solé,et al.  Distributed robustness in cellular networks: insights from synthetic evolved circuits , 2009, Journal of The Royal Society Interface.

[20]  Andreas Wagner,et al.  The molecular origins of evolutionary innovations. , 2011, Trends in genetics : TIG.

[21]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Julian Francis Miller,et al.  Redundancy and computational efficiency in Cartesian genetic programming , 2006, IEEE Transactions on Evolutionary Computation.

[23]  Cheyenne L. Laue,et al.  Landscape Revolutions for Cultural Evolution: Integrating Advanced Fitness Landscapes into the Study of Cultural Change , 2019, Handbook of Evolutionary Research in Archaeology.

[24]  Erik Kaestner,et al.  The Origins Of Genome Architecture , 2016 .

[25]  Marco Tomassini,et al.  A network perspective on genotype–phenotype mapping in genetic programming , 2020, Genetic Programming and Evolvable Machines.

[26]  S. Ahnert Structural properties of genotype–phenotype maps , 2017, Journal of The Royal Society Interface.

[27]  S. Ahnert,et al.  A tractable genotype-phenotype map for the self-assembly of protein quaternary structure , 2013, 1311.0399.

[28]  Ting Hu,et al.  Neutrality, Robustness, and Evolvability in Genetic Programming , 2016, GPTP.

[29]  Gunther J. Eble,et al.  The evolution of complexity , 2001, Complex..

[30]  Andreas Wagner,et al.  Adding levels of complexity enhances robustness and evolvability in a multilevel genotype–phenotype map , 2017, Journal of The Royal Society Interface.

[31]  Andreas Wagner,et al.  The potential for non-adaptive origins of evolutionary innovations in central carbon metabolism , 2016, BMC Systems Biology.

[32]  Julian Francis Miller,et al.  The Advantages of Landscape Neutrality in Digital Circuit Evolution , 2000, ICES.