Adaptive Exploration in Lenia with Intrinsic Multi-Objective Ranking

2025-06-03

AI Summary

Open-ended evolution in Lenia—a continuous cellular automaton—faces the core challenge of achieving sustained, unconstrained innovation without prior assumptions or fixed objectives. Method: We propose an intrinsically motivated, multi-objective ranking–driven adaptive exploration mechanism that replaces scalar fitness with joint optimization of behavioral novelty, descriptor-space sparsity (estimated via kernel density estimation), and population homeostasis. The approach integrates Lenia’s dynamical modeling, intrinsic motivation principles, Pareto-based multi-objective sorting, and a homeostatic regulation model. Contribution/Results: Our method enables unbounded, diverse, and self-sustaining evolutionary trajectories. Experiments demonstrate significant improvements in long-term population diversity and novelty, consistently generating complex, non-repetitive, life-like dynamic patterns. It establishes a scalable, reproducible paradigm for open-ended evolution in artificial life, advancing beyond hand-crafted fitness functions toward autonomous, objective-free evolutionary discovery.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Promote exploration in Lenia cellular automata evolution
• Achieve unbounded innovation using multi-objective ranking
• Enable open-ended evolution in artificial life systems

Innovation

• Multi-objective ranking for intrinsic fitness
• Adaptive exploration in descriptor space
• Continuous cellular automata evolution