Adaptive Exploration in Lenia with Intrinsic Multi-Objective Ranking

TL;DR

This paper introduces a multi-objective intrinsic ranking method to enhance exploration and innovation in Lenia cellular automata, advancing the pursuit of open-ended artificial life evolution.

Contribution

It proposes a novel multi-objective intrinsic fitness framework that promotes continuous exploration and diversity in Lenia, addressing key challenges in artificial life evolution.

Findings

Multi-objective ranking encourages persistent exploration.

Intrinsic evolution fosters diverse emergent behaviors.

Adaptive exploration improves evolutionary dynamics.

Abstract

Artificial life aims to understand the fundamental principles of biological life by creating computational models that exhibit life-like properties. Although artificial life systems show promise for simulating biological evolution, achieving open-endedness remains a central challenge. This work investigates mechanisms to promote exploration and unbounded innovation within evolving populations of Lenia continuous cellular automata by evaluating individuals against each other with respect to distinctiveness, population sparsity, and homeostatic regulation. Multi-objective ranking of these intrinsic fitness objectives encourages the perpetual selection of novel and explorative individuals in sparse regions of the descriptor space without restricting the scope of emergent behaviors. We present experiments demonstrating the effectiveness of our multi-objective approach and emphasize that intrinsic evolution allows diverse expressions of artificial life to emerge. We argue that adaptive exploration improves evolutionary dynamics and serves as an important step toward achieving open-ended evolution in artificial systems.