title: The genotype-phenotype map of an evolving digital organism.
abstract: To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.
authors.affiliation: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
authors.forename: Miguel A
authors.identifier: 0000-0002-8374-1941
authors.initials: MA
authors.lastname: Fortuna
authors.affiliation: Department of Biology, University of Washington, Seattle, Washington, United States of America.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, Washington, United States of America.
authors.forename: Luis
authors.initials: L
authors.lastname: Zaman
authors.affiliation: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, Washington, United States of America.|Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, Washington, United States of America.
authors.forename: Charles
authors.identifier: 0000-0003-2924-1732
authors.initials: C
authors.lastname: Ofria
authors.affiliation: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|The Santa Fe Institute, Santa Fe, New Mexico, Washington, United States of America.
authors.forename: Andreas
authors.initials: A
authors.lastname: Wagner
mesh_terms: D005075:Biological Evolution; D003198:Computer Simulation; D019143:Evolution, Molecular; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D012641:Selection, Genetic / Q000235:genetics*; D058615:Synthetic Biology / Q000379:methods*