This paper presents a framework called Program Search for Financial Trading (ProFiT), a large-language-model-driven evolutionary algorithm for automated discovery and continual improvement of trading strategies in financial markets. These markets are inherently non-stationary and thus resist static modeling or prediction, suggesting the need for a more open-ended and adaptive evolutionary approach. ProFiT integrates code-level mutation on a complexifying representation, self-analysis, and walk-forward validation within a closed feedback loop, enabling trading strategies to autonomously evolve in response to changing market conditions. ProFiT consistently outperforms both Random and Buy-and-Hold strategies, which are used as both academic and industry-standard baselines, across seven futures assets. Specifically, it outperforms Random in 100% of cases and surpasses Buy-and-Hold in over 77% of all evolved strategy-asset combinations. Collectively, these results demonstrate that the ProFiT framework yields robust, statistically significant, and risk-adjusted gains across diverse types of financial assets and market dynamics, establishing a practical pathway toward open-ended self-improving algorithmic trading systems.

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversity algorithms are commonly used for this purpose, as they can provide diverse suggestions that represent salient areas of the solution space, showcasing designs with high fitness and wide variety. Because generated suggestions drive the search process, it is important that they provide inspiration, but also stay aligned with the designer's intentions. Additionally, often many interactions with the system are required before the designer is content with a solution. In this work, we tackle these challenges with an interactive constrained MAP-Elites system that leverages emitters to learn the preferences of the designer and then use them in automated steps. By learning preferences, the generated designs remain aligned with the designer's intent, and by applying automatic steps, we generate more solutions per user interaction, giving a larger number of choices to the designer and thereby speeding up the search. We propose a general framework for preference-learning emitters (PLEs) and apply it to a procedural content generation task in the video game Space Engineers. We built an interactive application for our algorithm and performed a user study with players.

POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) is a system for open-ended learning through neuroevolution in game-based domains. It builds on the Paired Open-Ended Trailblazer (POET) system, which originally explored learning and environment generation for bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI) system. Previous work showed that by coevolving levels and neural network policies, levels could be found for which successful policies could not be created via optimization alone. Studied in the realm of artificial life as a potentially open-ended alternative to gradient-based fitness, minimal criteria (MC)-based selection helps foster diversity in evolutionary populations. The main question addressed by this article is how the open-ended learning actually works, focusing in particular on the role of transfer of policies from one evolutionary branch (“species”) to another. We analyze the dynamics of the system through creating phylogenetic trees, analyzing evolutionary trajectories of policies, and temporally breaking down transfers according to species type. Furthermore, we analyze the impact of the minimal criterion on generated level diversity and interspecies transfer. The most insightful finding is that interspecies transfer, while rare, is crucial to the system's success.

When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the feasible-infeasible two-population (FI-2Pop) constrained optimisation algorithm for PCG, sometimes in combination with the multi-dimensional archive of phenotypic-elites (MAP-Elites) algorithm for finding a set of diverse solutions. However, the fitness function for the infeasible population only takes into account the number of constraints violated. In this paper we present a variant of FI-2Pop in which a surrogate model is trained to predict the fitness of feasible children from infeasible parents, weighted by the probability of producing feasible children. This drives selection towards higher-fitness, feasible solutions. We demonstrate our method on the task of generating spaceships for Space Engineers, showing improvements over both standard FI-2Pop, and the more recent multi-emitter constrained MAP-Elites algorithm.

This paper introduces ChemGrid, which is a novel video-game-based domain for exploring open-ended artificial intelligence capable of setting arbitrary goals for itself. In this domain, agents must satisfy a minimal goal of crafting a preset “survival molecule” but otherwise can pursue their own agendas related to crafting in an artificial chemistry based on the concept of pathway complexity from artificial life. In particular, agents can discover new molecular recipes by joining existing recipes together and breaking them apart. This paper explores the design space afforded by the artificial chemistry. The main contribution is the introduction of ChemGrid itself, which is intended as a future benchmark domain for general game-playing algorithms.

CLIPDraw is an algorithm that synthesizes novel drawings from natural language input. It does not require any additional training; rather, a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, which biases drawings towards simpler human-recognizable shapes. Results compare CLIPDraw with other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw.

This special issue highlights key selections from the 2020 Conference on Artificial Life, which is the primary international meeting organized yearly by the International Society for Artificial Life (www.alife.org). The conference themes broadly address the synthesis and simulation of living systems, welcoming scientific research that either deepens our understanding of life as we know it or broadens our conception of life as it could be (Langton, 1989).The 2020 conference, hosted by the University of Vermont and the Vermont Complex Systems Center, was originally intended to be held in Montréal, Québec, Canada. However, the global COVID-19 pandemic forced this event, like many others, to be held online. In truth, this challenge afforded a unique opportunity to hold a truly global conference, with 390 registered attendees from around the world.Of 183 submissions, 75 articles (41%) were accepted for full presentations at the conference and published in the proceedings (Bongard et al., 2020). An additional 11 submissions were presented as lighting talks and 24 as posters and were also included in the proceedings.Reflecting the highly interdisciplinary nature of the field, the topics covered in this special issue include evolutionary dynamics, artificial chemistry, agent-based modelling, game theory, genetic programming, neuroevolution, embodiment, and complex systems research: Ghouri, Barnes, and Lewis present a minimal version of the classic river crossing task, isolating the core task of building a bridge in a grid world for the purpose of increasing explainability of the original problem. Results with the minimal environment are consistent with results from the original version, highlighting the utility of the minimal environment for experiments on explainable evolutionary intelligence.Grove, Timbrell, Jolley, Polack, and Borg demonstrate that the mathematical color of noise in an environment has a significant impact on dynamics in evolving populations. In particular, their results call into question whether commonly employed Gaussian or white noise models should be the default.Lexicase selection in genetic programming is an alternative to traditional parent selection. Helmuth and Spector conduct an extensive benchmarking of a variant called down-sampled lexicase selection, showing that it outperforms standard selection, and investigate hypotheses about why it performs so well.Howison, Hugues, and Iida explore how morphology can be used to control and program interactions with the environment in their study of V-shaped falling papers. They also show how Bayesian optimization can be used to design functional constructs of nonliving materials in the real world.Hudcová and Mikolov provide a framework for classifying cellular automata complexity based on transients, which are parts of automata trajectories observed before entering into a loop. In particular, the presented classification is based on the asymptotic growth of the average transient length with increasing grid size. This framework is intended to aid the identification of interesting phenomena in evolving systems.Krellner and Han study the evolution of cooperation among agents playing a donation game. In particular, this work introduces a novel approach to information sharing to solve the problem of private information.Kruszewski and Mikolov develop an artificial chemistry based on combinatory logic, showing that complex structures emerge over time from a simple dynamical system. This work explicitly addresses the origins of open-ended evolution, which is a longstanding pursuit for the field of Artificial Life (and science in general).Miller re-envisions the artificial neuron model to better reflect natural evolution and development processes. Using this model, evolved programs can construct artificial neural networks that can be broken down into smaller networks that each solve distinct tasks.The 2020 conference theme “New frontiers in AI: What can ALife offer AI?” asked the community to consider how the unbridled and sometimes unconventional creativity of Artificial Life research might inspire innovation in mainstream Artificial Intelligence. In fact, the two fields share a deeply intertwined history, as some of the greatest pioneers in early Artificial Intelligence work also (or first) pursued what would now be called Artificial Life. As an example, Shannon (1940), often referred to as the father of information theory, wrote his doctoral dissertation An Algebra for Theoretical Genetics 16 years before he helped found the field of Artificial Intelligence at the Dartmouth Conference.At the same time, the Artificial Life community continues in its own myriad pursuits, recapitulating and reinventing nature often with computational tools, as evidenced by the works contained in this volume. Evolution on Earth gave rise to natural intelligence, and so evolution in silico (a mainstay of Artificial Life research) similarly bears the potential for creating Artificial Intelligence open-endedly; such is the foundational assumption of research on open-ended evolution. What ALife can offer AI is, among other things, an invitation to question what about life and intelligence might transcend substrates and, in doing so, to discover how what is might inspire what will be.

Understanding and engineering open-endedness, or the indefinite generation of novelty and complexity at arbitrary scales, has long been studied by implementing nature-inspired simulations specifically designed for artificial life studies. This paper argues that video games serve as a complementary domain for research on open-endedness. In support of this claim, experiments in this paper evaluate the effects of age-based and spatial destructive events in two game domains: an interactive Game of Life and the city-building game SimCity. These games are played by a neural-network-controlled gameplay agent trying to maximize reward. Results indicate that experiments with SimCity are more likely to identify statistically significant differences in complexity as a result of applied destructive events, highlighting the utility of this game domain for studying artificial life phenomena.

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary.

Because the kind of open-ended complexity explosion seen on Earth remains beyond the observed dynamics of current artificial life worlds, it has become critical to isolate and investigate specific ...

In contrast to the conventional role of evolution in evolutionary computation (EC) as an optimization algorithm, a new class of evolutionary algorithms has emerged in recent years that instead aim to accumulate as diverse a collection of discoveries as possible, yet where each variant in the collection is as fit as it can be. Often applied in both neuroevolution and morphological evolution, these new quality diversity (QD) algorithms are particularly well-suited to evolution's inherent strengths, thereby offering a promising niche for EC within the broader field of machine learning. However, because QD algorithms are so new, until now no comprehensive study has yet attempted to systematically elucidate their relative strengths and weaknesses under different conditions. Taking a first step in this direction, this paper introduces a new benchmark domain designed specifically to compare and contrast QD algorithms. It then shows how the degree of alignment between the measure of quality and the behavior characterization (which is an essential component of all QD algorithms to date) impacts the ultimate performance of different such algorithms. The hope is that this initial study will help to stimulate interest in QD and begin to unify the disparate ideas in the area.