Capturing Emerging Complexity in Lenia
Sanyam Jain, Aarati Shrestha, Stefano Nichele

TL;DR
This paper explores measuring and evolving complexity in Lenia, an artificial life platform, using genetic algorithms and novel fitness functions to discover new behaviors and understand emergent ecosystem dynamics.
Contribution
It introduces new metrics and evolutionary methods for measuring and enhancing complexity in Lenia, facilitating the discovery of previously unknown behaviors.
Findings
Kernel's center of mass tends to increase with specific pixel sets.
Kernels evolve towards a Gaussian distribution with borders.
Evolved behaviors show increased complexity over generations.
Abstract
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…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Genetics, Aging, and Longevity in Model Organisms · Cephalopods and Marine Biology
