The genotype-phenotype map of an evolving digital organism.

Miguel A Fortuna,Luis Zaman,Andreas Wagner,Charles Ofria,Claus O Wilke

doi:10.1371/journal.pcbi.1005414

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.

Highlights

In natural and artificial systems that undergo Darwinian evolution by random mutation and selection, a central distinction is that between a genotype and a phenotype
The larger any one such network is, the greater is the number of novel phenotypes that can be reached through single point mutations from its members
We find that the accessibility of novel phenotypes is highly asymmetric: it is much harder to evolve more complex phenotypes than simpler ones through single point mutations

Summary

Introduction

In natural and artificial systems that undergo Darwinian evolution by random mutation and selection, a central distinction is that between a genotype (the entire set of genetic material or a digital organism’s set of instructions, respectively) and a phenotype (the set of observable traits encoded by the genotype). This distinction is important for two main reasons. They influence what kind of variation becomes available to natural selection They constrain the directions of evolutionary change. They affect the likelihood that new and beneficial phenotypes—evolutionary adaptations and innovations—originate in the first place [1,2,3,4,5]

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