David M. Bryson

Coevolution drives the emergence of complex traits and promotes evolvability.

Experiments using a digital host-parasite model system show that coevolution can drive the emergence of complex traits and more evolvable genomes. Homepage Title: Parasitism Drives the Evolution of Complexity

From Cues to Signals: Evolution of Interspecific Communication via Aposematism and Mimicry in a Predator-Prey System

Current theory suggests that many signaling systems evolved from preexisting cues. In aposematic systems, prey warning signals benefit both predator and prey. When the signal is highly beneficial, a third species often evolves to mimic the toxic species, exploiting the signaling system for its own protection. We investigated the evolutionary dynamics of predator cue utilization and prey signaling in a digital predator-prey system in which prey could evolve to alter their appearance to mimic poison-free or poisonous prey. In predators, we observed rapid evolution of cue recognition (i.e. active behavioral responses) when presented with sufficiently poisonous prey. In addition, active signaling (i.e. mimicry) evolved in prey under all conditions that led to cue utilization. Thus we show that despite imperfect and dishonest signaling, given a high cost of consuming poisonous prey, complex systems of interspecific communication can evolve via predator cue recognition and prey signal manipulation. This provides evidence supporting hypotheses that cues may serve as stepping-stones in the evolution of more advanced communication and signaling systems that incorporate information about the environment.

Understanding evolutionary potential in virtual CPU instruction set architectures

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges.

A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms

Investigating the evolution of animal behavior is difficult. The fossil record leaves few clues that would allow us to recapitulate the path that evolution took to build a complex behavior, and the large population sizes and long time scales required prevent us from re-evolving such behaviors in a laboratory setting. We present results of a study in which digital organisms–self-replicating computer programs that are subject to mutations and selection–evolved in different environments that required information about past experience for fitness-enhancing behavioral decisions. One population evolved a mechanism for step-counting, a surprisingly complex odometric behavior that was only indirectly related to enhancing fitness. We examine in detail the operation of the evolved mechanism and the evolutionary transitions that produced this striking example of a complex behavior.

Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions

When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual in- struction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary poten- tial of genotypes experiencing environmental change. We observed surprising robustness to these poor design deci- sions across all seven environments designed to analyze a wide range challenges. Analysis of the short term evolution- ary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative ef- fects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change.

There and back again: gene-processing hardware for the evolution and robotic deployment of robust navigation strategies

Navigation strategies represent some of the most intriguing examples of complex and intelligent behaviors in nature. Accordingly, they have been the focus of extensive research in animal behavior and in evolutionary robotics. However, engineering successes in harnessing the evolutionary dynamics that shape sophisticated navigation strategies remain limited. Here we describe a novel gene-processing architecture for digital organisms that enables the evolution of central-place-foraging strategies, such as those seen in honeybees and striped hyena. While previous studies have evolved navigation de novo, the resulting algorithms have been relatively fragile and difficult to translate into physical systems. In contrast, the strategies evolved in this study are highly congruous with those seen in nature: a single evolved foraging strategy incorporates periods of directed travel, fixed pattern search, cue response, and reorientation when outcomes do not match expected results. Additionally, the genetic architecture enabled rapid extraction of the underlying behavioral algorithm and transference to a robotic system, proving to be robust to issues of noise and scale that commonly plague such attempts. Accordingly, we demonstrate that the flexibility and interpretability of the new gene-processing hardware readily facilitate the creation, study, and utilization of naturalistic and deployable algorithms for functionally complex behaviors.