Inspired by biological and cultural evolution, there have been many attempts to explore and elucidate the necessary conditions for open-endedness in artificial intelligence and artificial life. Using a continuous cellular automata called Lenia as the base system, we built large-scale evolutionary simulations using parallel computing framework JAX, in order to achieve the goal of never-ending evolution of self-organizing patterns. We report a number of system design choices, including (1) implicit implementation of genetic operators, such as reproduction by pattern self-replication, and selection by differential existential success; (2) localization of genetic information; and (3) algorithms for dynamically maintenance of the localized genotypes and translation to phenotypes. Simulation results tend to go through a phase of diversity and creativity, gradually converge to domination by fast expanding patterns, presumably a optimal solution under the current design. Based on our experimentation, we propose several factors that may further facilitate open-ended evolution, such as virtual environment design, mass conservation, and energy constraints.

翻译：受生物与文化的进化启发，学界已进行诸多探索，旨在揭示人工智能与人工生命领域中实现开放式进化（open-ended evolution）所需的条件。本研究以名为Lenia的连续元胞自动机为基础系统，借助并行计算框架JAX构建大规模进化仿真，致力于实现自组织模式永无止境的演化。我们报告了若干系统设计选择，包括：（1）遗传算子的隐式实现，如通过模式自我复制的繁殖机制及基于生存差异的选择机制；（2）遗传信息的局域化；（3）动态维护局域基因型并将其翻译为表型的算法。仿真结果往往经历多样性与创造性阶段，并逐渐收敛于快速扩张模式的主导——这可能是当前设计下的最优解。基于实验观察，我们提出若干可能进一步促进开放式进化的因素，如虚拟环境设计、质量守恒与能量约束。