A Study of the Coevolution of Digital Organisms with an Evolutionary Cellular Automaton

This paper presents an Evolutionary Cellular Automaton (ECA) that simulates the evolutionary dynamics of biological interactions by manipulating strategies of dispersion and associations between digital organisms. The parameterization of the different types of interaction and distribution strategies using configuration files generates easily interpretable results. In that respect, ECA is an effective instrument for measuring the effects of relative adaptive advantages and a good resource for studying natural selection. Although ECA works effectively in obtaining the expected results from most well-known biological interactions, some unexpected effects were observed. For example, organisms uniformly distributed in fragmented habitats do not favor eusociality, and mutualism evolved from parasitism simply by varying phenotypic flexibility. Finally, we have verified that natural selection represents a cost for the emergence of sex by destabilizing the stable evolutionary strategy of the 1:1 sex ratio after generating randomly different distributions in each generation.

How the footprint of history shapes the evolution of digital organisms

Gould’s thought experiment of “replaying life’s tape” provides a conceptual framework for experiments that quantify the contributions of adaptation, chance, and history to evolutionary outcomes. For example, we can empirically measure how varying the depth of history in one environment influences subsequent evolution in a new environment. Can this “footprint of history”—the genomic legacy of prior adaptation—grow too deep to overcome? Can it constrain adaptation, even with intense selection in the new environment? We investigated these questions using digital organisms. Specifically, we evolved ten populations from one ancestor under identical conditions. We then replayed evolution from three time points in each population’s history (corresponding to shallow, intermediate, and deep history) in two new environments (one similar and one dissimilar to the prior environment). We measured the contributions of adaptation, chance, and history to the among-lineage variation in fitness and genome length in both new environments. In both environments, variation in genome length depended largely on history and chance, not adaptation, indicating weak selection. By contrast, adaptation, chance, and history all contributed to variation in fitness. Crucially, whether the depth of history affected adaptation depended on the environment. When the ancestral and new environments overlapped, history was as important as adaptation to the fitness achieved in the new environment for the populations with the deepest history. However, when the ancestral and novel environments favored different traits, adaptation overwhelmed even deep history. This experimental design for assessing the influence of the depth of history is promising for both biological and digital systems.

An interplay of resource availability, population size and mutation rate potentiates the evolution of metabolic signaling

BackgroundAsexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift. Environmental conditions in which the evolution takes place define the emergent fitness landscapes. In this work, we used Avida—a digital evolution framework—to uncover a hitherto unexplored interaction between mutation rates, population size, and the relative abundance of metabolizable resources, and its effect on evolutionary outcomes in small populations of digital organisms.ResultsOver each simulation, the population evolved to one of several states, each associated with a single dominant phenotype with its associated fitness and genotype. For a low mutation rate, acquisition of fitness by organisms was accompanied with, and dependent on, an increase in rate of genomic replication. At an increased mutation rate, phenotypes with high fitness values were similarly achieved through enhanced genome replication rates. In addition, we also observed the frequent emergence of suboptimal fitness phenotype, wherein neighboring organisms signaled to each other information relevant to performing metabolic tasks. This metabolic signaling was vital to fitness acquisition and was correlated with greater genotypic and phenotypic heterogeneity in the population. The frequency of appearance of signaling populations increased with population size and with resource abundance.ConclusionsOur results reveal a minimal set of environment–genotype interactions that lead to the emergence of metabolic signaling within evolving populations.

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

The Complexity Ratchet: Stronger than Selection, Stronger than Evolvability, Weaker than Robustness

Using the in silico experimental evolution platform Aevol, we have tested the existence of a complexity ratchet by evolving populations of digital organisms under environmental conditions in which simple organisms can very well thrive and reproduce. We observed that in most simulations, organisms become complex although such organisms are a lot less fit than simple ones and have no robustness or evolvability advantage. This excludes selection from the set of possible explanations for the evolution of complexity. However, complementary experiments showed that selection is nevertheless necessary for complexity to evolve, also excluding non-selective effects. Analyzing the long-term fate of complex organisms, we showed that complex organisms almost never switch back to simplicity despite the potential fitness benefit. On the contrary, they consistently accumulate complexity in the long term, meanwhile slowly increasing their fitness but never overtaking that of simple organisms. This suggests the existence of a complexity ratchet powered by negative epistasis: Mutations leading to simple solutions, which are favorable at the beginning of the simulation, become deleterious after other mutations—leading to complex solutions—have been fixed. This also suggests that this complexity ratchet cannot be beaten by selection, but that it can be overthrown by robustness because of the constraints it imposes on the coding capacity of the genome.

Evaluating the Use of Avida-ED Digital Organisms to Teach Evolution & Natural Selection

The concepts of evolution and natural selection remain as some of the most challenging topics to teach. The difficulty in teaching these topics arises from the fact that evolution is difficult to observe, and computer simulations do not always result in a clear understanding of evolutionary principles. Recently, the Avida-ED software has been developed to simulate evolution in a laboratory setting. Unlike other simulations, Avida-ED allows students to manipulate the environment, change the genetics of the virtual organisms, and track offspring in real time. We have demonstrated, by using pretest and posttest questionnaires, that students gained a deeper understanding of evolutionary concepts by using this software. In particular, students showed the greatest increase in their ability to explain evolutionary concepts in answers to open-ended questions. Our results show that Avida-ED could be a useful tool in helping students understand and combat preconceived notions about evolution.

The epistemic role of fiction in scientific models

Giere?s analysis of the epistemic role of fiction in science and literature is the representative of antifictionists. Our research finds the three inconsistencies in his main paper regarding the comparison of fiction in scientific models and literary works. We analyze his argument and offer our solution to the issue favoring the perspective of fictionalism. Further, we support a typological differentiation of false representation in science into fictional and fictitious. The value of this differentiation we demonstrate by giving the example of digital organisms in system biology. The paper aims to help better understanding of fiction in science and to avoid the oversimplification of literary fiction.