This research project investigates Lenia, an artificial life platform that simulates ecosystems of digital creatures. Lenia's ecosystem consists of simple, artificial organisms that can move, consume, grow, and reproduce. The platform is important as a tool for studying artificial life and evolution, as it provides a scalable and flexible environment for creating a diverse range of organisms with varying abilities and behaviors. Measuring complexity in Lenia is a key aspect of the study, which identifies the metrics for measuring long-term complex emerging behavior of rules, with the aim of evolving better Lenia behaviors which are yet not discovered. The Genetic Algorithm uses neighborhoods or kernels as genotype while keeping the rest of the parameters of Lenia as fixed, for example growth function, to produce different behaviors respective to the population and then measures fitness value to decide the complexity of the resulting behavior. First, we use Variation over Time as a fitness function where higher variance between the frames are rewarded. Second, we use Auto-encoder based fitness where variation of the list of reconstruction loss for the frames is rewarded. Third, we perform combined fitness where higher variation of the pixel density of reconstructed frames is rewarded. All three experiments are tweaked with pixel alive threshold and frames used. Finally, after performing nine experiments of each fitness for 500 generations, we pick configurations from all experiments such that there is a scope of further evolution, and run it for 2500 generations. Results show that the kernel's center of mass increases with a specific set of pixels and together with borders the kernel try to achieve a Gaussian distribution. Results are available at https://s4nyam.github.io/evolenia/

翻译：本研究项目探讨了Lenia——一个模拟数字生物生态系统的人工生命平台。Lenia的生态系统由简单的、可移动、摄食、生长和繁殖的人工生物组成。该平台作为研究人工生命与进化的重要工具，提供了可扩展且灵活的环境，能够生成具有多样能力与行为的生物群体。衡量Lenia中的复杂性是研究的核心，我们确定了用于量化规则长期涌现复杂行为的指标，旨在进化出尚未发现的更优Lenia行为。遗传算法采用邻域或内核作为基因型，同时固定Lenia的其他参数（例如生长函数），根据种群生成不同行为，并通过计算适应度值来判定行为的复杂性。首先，我们以时间变化率作为适应度函数，奖励帧间方差较大的行为；其次，采用基于自编码器的适应度，奖励重建损失列表方差较大的行为；第三，执行组合适应度，奖励重建帧像素密度方差较大的行为。通过调整像素存活阈值和帧数优化三种实验。最终，在每种适应度下运行500代共九组实验后，选取所有实验中仍有进化潜力的配置，将其扩展至2500代。结果表明，内核质心随特定像素集增加而上升，且内核边界趋于呈现高斯分布。实验结果详见 https://s4nyam.github.io/evolenia/