Tokyo Type 1 open-ended evolution (OEE) is a category of OEE that includes systems exhibiting the ongoing generation of adaptive novelty and ongoing growth in complexity. It can be considered as a necessary foundation for Tokyo Type 2 OEE (ongoing evolution of evolvability) and Tokyo Type 3 OEE (ongoing generation of major transitions). This article brings together five methods of analysis to form a procedure for testing for Tokyo Type 1 OEE. The procedure is presented as simply as possible, isolated from the complexities of any particular evolutionary system, and with a clear rationale for each step. In developing these steps, we also identify five key challenges in OEE. The last of these (achieving a higher order of complexity growth within a system exhibiting indefinitely scalable complexity) can be considered a grand challenge for Tokyo Type 1 OEE. Promising approaches to this grand challenge include also achieving one or both of Tokyo Types 2 and 3 OEE; this can be seen as one answer to why these other types of OEE are important, providing a unified view of OEE.

In a recent article by Borg and Channon it was shown that social information alone, decoupled from any within-lifetime learning, can result in improved performance on a food-foraging task compared to when social information is unavailable. Here we assess whether access to social information leads to significant behavioral differences both when access to social information leads to improved performance on the task, and when it does not: Do any behaviors resulting from social information use, such as movement and increased agent interaction, persist even when the ability to discriminate between poisonous and non-poisonous food is no better than when social information is unavailable? Using a neuroevolutionary artificial life simulation, we show that social information use can lead to the emergence of behaviors that differ from when social information is unavailable, and that these behaviors act as a promoter of agent interaction. The results presented here suggest that the introduction of social information is sufficient, even when decoupled from within-lifetime learning, for the emergence of pro-social behaviors. We believe this work to be the first use of an artificial evolutionary system to explore the behavioral consequences of social information use in the absence of within-lifetime learning.

Geb was the first artificial life system to be classified as exhibiting open-ended evolutionary dynamics according to Bedau and Packard's evolutionary activity measures and is the only one to have been classified as such according to the enhanced version of that classification scheme. Its evolution is driven by biotic selection, that is (approximately), by natural selection rather than artificial selection. Whether or not Geb can generate an indefinite increase in maximum individual complexity is evaluated here by scaling two parameters: world length (which bounds population size) and the maximum number of neurons per individual. Maximum individual complexity is found to be asymptotically bounded when scaling either parameter alone. However, maximum individual complexity is found to be indefinitely scalable, to the extent evaluated so far (with run times in years and billions of reproductions per run), when scaling both world length and the maximum number of neurons per individual together. Further, maximum individual complexity is shown to scale logarithmically with (the lower of) maximum population size and maximum number of neurons per individual. This raises interesting questions and lines of thought about the feasibility of achieving complex results within open-ended evolutionary systems and how to improve on this order of complexity growth.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving . It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving . It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .