Bipedal hopping is an efficient form of locomotion, yet it remains relatively rare in the natural world. Previous research has suggested that the tail balances the angular momentum of the legs to produce steady state bipedal hopping. In this study, we employ a 3D physics simulation engine to optimize gaits for an animat whose control and morphological characteristics are subject to computational evolution, which emulates properties of natural evolution. Results indicate that the order of gene fixation during the evolutionary process influences whether a bipedal hopping or quadrupedal bounding gait emerges. Furthermore, we found that in the most effective bipedal hoppers the tail balances the angular momentum of the torso, rather than the legs as previously thought. Finally, there appears to be a specific range of tail masses, as a proportion of total body mass, wherein the most effective bipedal hoppers evolve.

We investigate a hierarchical approach to robot control inspired by joint-level control in animals. The method combines a high-level controller, consisting of an artificial neural network (ANN), with joint-level controllers based on digital muscles . In the digital muscle model (DMM), morphological and control aspects of joints evolve concurrently, emulating the musculoskeletal system of natural organisms. We introduce and compare different approaches for connecting outputs of the ANN to DMM-based joints. We also compare the performance of evolved animats with ANN-DMM controllers with those governed by only high-level (ANN-only) and low-level (DMM-only) controllers. These results show that DMM-based systems outperform their ANN-only counterparts while also exhibiting less complex ANNs in terms of the number of connections and neurons. The main contribution of this work is to explore the evolution of artificial systems where, as in natural organisms, some aspects of control are realized at the joint level.

Quorum sensing (QS) is a collective behavior whereby actions of individuals depend on the density of the surrounding population. Bacteria use QS to trigger secretion of digestive enzymes, formation and destruction of biofilms, and, in the case of pathogenic organisms, expression of virulence factors that cause disease. Investigations of mechanisms that prevent or disrupt QS, referred to as quorum quenching , are of interest because they provide a new alternative to antibiotics for treating bacterial infections. Traditional antibiotics either kill bacteria or inhibit their growth, producing selective pressures that promote resistant strains. In contrast, quorum quenching and other so-called anti-infective strategies focus on altering behavior. In this article we evolve QS in populations of digital organisms, a type of self-replicating computer program, and investigate the effects of quorum quenching on these populations. Specifically, we injected the populations with mutant organisms that were impaired in selected ways to disrupt the QS process. The experimental results indicate that the rate at which these mutants are introduced into a population influences both the evolvability of QS and the persistence of an existing QS behavior. Surprisingly, we also observed resistance to quorum quenching. Effectively, populations evolved resistance by reaching quorum at lower cell densities than did the parent strain. Moreover, the level of resistance was highest when the rate of mutant introduction increased over time. These results show that digital organisms can serve as a model to study the evolution and disruption of QS, potentially informing wet-lab studies aimed at identifying targets for anti-infective development.

We present a study in the evolution of temporal behavior, specifically synchronization and desynchronization, through digital evolution and group selection. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. Group selection links the survival of the individual to the survival of its group, thus encouraging cooperation. Previous approaches to engineering synchronization and desynchronization algorithms have taken inspiration from nature: In the well-known firefly model, the only form of communication between agents is in the form of flash messages among neighbors. Here we demonstrate that populations of digital organisms, provided with a similar mechanism and minimal information about their environment, are capable of evolving algorithms for synchronization and desynchronization, and that the evolved behaviors are robust to message loss. We further describe how the evolved behavior for synchronization mimics that of the well-known Ermentrout model for firefly synchronization in biology. In addition to discovering self-organizing behaviors for distributed computing systems, this result indicates that digital evolution may be used to further our understanding of synchronization in biology.