Open-Ended Evolution in Cellular Automata Worlds

Open-ended evolution is a fundamental issue in artificial life research. We consider biological and social systems as a flux of interacting components that transiently participate in interactions with other system components as part of these systems. This approach and the corresponding reasoning suggest that systems able to deliver open-ended evolution must have a representation equivalent of Turing machines. Here we provide an implementation of a such model of evolving systems using a cellular automata world. We analyze the simulated world using a set of metrics based on criteria of open-ended evolution suggested by Bedau et al. We show that the cellular automata world has significantly more evolutionary activity than a corresponding random shadow world. Our work indicates that the proposed cellular automata worlds have the potential to generate open-ended evolution according to the criteria that we have considered

Virtual reality in biology : could we become virtual naturalists?

Acknowledgements We would like to thank editors and anonymous reviewers for insightful comments that improved readability of our manuscript. In particular, we would like to thank Reviewer #1 whose comments made the entire peer-review a stimulating and productive process. We would like to acknowledge Lucy C. Kerr for kindly proof-reading the final version of this manuscript.Peer reviewedPublisher PD

Increase and Maintenance of Community Diversity by Positive Frequency-dependent Predation in the Tierra System

Ecological communities often contain a wide diversity of species but how different species may arise and stably coexist, especially in homogenous spatial environments, is poorly understood. In this dissertation, I use the well-known digital life system, Tierra, to explore the influence of predation on community diversity in a homogenous environment. In order to introduce predation into the Tierra system, I design a digital predator whose survival and reproduction depend on the amount of CPU energy acquired through predation. This energy dependence of predators on their prey robustly generates a "Lotka-Volterra-like" cyclic oscillation in Tierra. This cyclic outcome suggests that the design of digital prey and predators may capture some essential properties of the predation relationship observed in nature.After predation is built into the Tierra system, I study two predation strategies that predators may use when encountering two or more different types of prey, namely, proportional predation and positive frequency-dependent predation. I block all the mutations in Tierra so that predators and prey interact with each other in an ecological scenario. The simulation results show that a predator population with positive frequency-dependent behavior maintains a stable coexistence of multiple competing prey species, but a predator population with proportional predation behavior fails to do so. Further studies on the underlying mechanisms of the maintenance of prey diversity reveal that by consuming disproportionately more of the common prey type than of the rare one, positive frequency-dependent predation essentially provides a strong negative feedback regulation on prey populations, which tends to equalize the abundances of different prey species and thus results in a stable persistence of prey diversity. Therefore, in contrast to the previous studies which questioned whether the mechanism of positive frequency-dependent predation functioned at a population level, the simulation results here strongly support that a population of frequency-dependent predators has the potential to maintain the diversity of prey species in nature.Besides their effects on maintaining prey diversity, the two predation strategies are further examined from the perspective of enhancing the fitness of predators when they feed on two different types of prey. The simulation results show that when the predators with one predation strategy become the dominant type, they change the relative abundance of two prey types in such a way that favors, rather than depresses, the predators with the other predation strategy. This mutual support, rather than exclusion, allows the two predation strategies to be comparably competitive and thus may coexist in the population or either one of them may go extinct.With the understanding of prey diversity maintained by a population of predators with positive frequency-dependent behavior in the ecological scenarios, I proceed to explore the changes in community diversity influenced by predators during evolution when various types of mutations in Tierra are turned on to allow digital creatures to evolve. The simulation results show that the community with persistent intensive predation robustly exhibits significantly higher diversity than the community without predation. Therefore, positive frequency-dependent predation may also be able to promote and maintain a high level of diversity in an evolving ecological community. In addition, with the presence of predation in the community, the sizes of digital creatures remain relatively constant during evolution, which prevents the loss of complex structures in the genomes of creatures as well as promoting rich interactions among creatures. This suggests that the community of prey and predators may maintain interesting ecological dynamics through longer periods of evolution. Furthermore, as digital creatures constantly adapt to their ever-changing biotic environment, a coevolving pattern between prey and predator populations spontaneously emerges in Tierra: prey mutate their templates to avoid being found by predators and predators evolve their templates to circumvent the escape strategy developed in the newly evolved prey. These reciprocal adaptations may continuously create new niches and thus drive the evolution of prey and predators in the digital community

Spéciation guidée par l'environnement‎ : interactions sur des périodes évolutionnaires de communautés de plantes artificielles

Depuis des décades, les chercheurs en Vie Artificielle on créé une pléthore de créatures en utilisant de multiples schémas d’encodage, capacités motrices et aptitudes cognitives. Un motif récurrent, cependant, est que la focalisation est centrée sur les individus à évoluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus évolutionnaire vers des régions de l’espace génétique plus robustes and diverses. Nous avons conçu un modèle morphologique complexe, basé sur les graphes orientés de K. Sims, qui repose sur le moteur physique Bullet pour la précision et utilise des contraintes à 6 Degrés de Liberté pour connecter les paires d’organes. Nous avons ainsi évolué un panel de plantes à l’aspect naturel qui devaient survivre malgré des niveaux de ressources variables induits par une source de lumière mobile et des motifs de pluies saisonnières. En plus de cette expérience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins d’herbe statiques. La complexité de ce modèle, cependant, ne permettait pas la mise a l’échelle d’une évolution de populations et a donc été réduit dans l’expérience suivante, notamment en supprimant le moteur physique. Cela nous a amené à l’exploration de la co-évolution de populations composées d’une unique espèce et ayant la capacité de se reproduire de manière autonome grâce à notre Bail-Out Crossover (Croisement avec Désistement). Bien que les populations résultantes n’ont pas démontré un grand intérêt pour cette aptitude, elles ont néanmoins fourni d’importantes informations sur les mécanismes d’auto-reproduction. Ceux-ci ont été mis en action dans un second modèle inspiré des travaux de Bornhofen. Grâce à sa légèreté, cela nous a permis de traiter non seulement de plus grandes populations (de l’ordre de milliers d’individus) mais aussi de plus longues périodes évolutionnaires (100 années, approximativement 5000 générations). Notre première expérience avec ce modèle s’est concentrée sur la possibilité de reproduire des cas d’école de spéciation (allopatrique, parapatrique, péripatrique) sur cette plate-forme. Grâce à APOGet, une nouvelle procédure de regroupement pour l’extraction en parallèle d’espèces à partir d’un arbre généalogique, nous avons pu affirmer que le système était effectivement capable de spéciation spontanée. Cela nous a conduit à une dernière expérience dans laquelle l’environnement était contrôlé par de la Programmation Génétique Cartésienne (CGP), permettant ainsi une évolution automatique d’une population et des contraintes abiotiques auxquelles elle était confrontée. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survécu à de brutales et imprévisibles variations environnementales. En les comparant à un groupe contrôle c pour lequel les contraintes ont été maintenues faibles et constantes, le groupe évolué e a montré des performances mitigées: dans les deux types de tests, une moitié de e surpassait c qui, à son tour, surpassait la moitié restante de e. Nous avons aussi trouvé une très forte corrélation entre les chutes catastrophiques de population et la performance des évolutions correspondantes. Il en résulte que l’évolution de population dans des environnements hostiles et dynamiques n’est pas une panacée bien que ces expériences en démontrent le potentiel et souligne le besoin d’études ultérieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Sims’ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp