Population Dynamics in ARIEL Robotics Systems Featuring Embodied Evolution via Spatial Mating Mechanisms

2026-04-29

AI Summary

This study investigates the influence of spatial structure on population dynamics and stability in embodied evolution. Conducted in a two-dimensional MuJoCo-based physical environment, the research employs HyperNEAT to evolve neural controllers for ARIEL gecko robots, incorporating spatial mating, energy-based selection, and density-dependent mortality mechanisms. The work reveals, for the first time, a continuous phase transition under energy-based selection, elucidates how the coupling of distinct selection mechanisms fundamentally affects population stability, and identifies critical constraints in designing spatial evolutionary algorithms. Experimental results demonstrate that spatial mating yields only a 4.9% increase in peak fitness compared to random pairing; while density-dependent mortality achieves a 97% task success rate, it concurrently reduces overall fitness; notably, only deterministic fitness-based selection sustains long-term population stability.

Abstract

We present a Spatially Embedded Evolutionary Algorithm where robot individuals exist in a physically simulated 2D environment, must navigate to encounter potential mates, and compete for survival under various spatially-aware selection pressures. Using HyperNEAT evolved neural controllers for ARIEL gecko-inspired quadrupeds in MuJoCo, we investigate how spatial structure fundamentally alters evolutionary dynamics. Our experiments show a modest 4.9% difference in peak fitness between proximity-based and random pairing possibly within stochastic variation while combining spatial parent selection with stochastic death selection produces unstable population dynamics. We discover a continuous phase transition in energy-based selection experiments, with critical zone count separating extinction-dominated and explosion-dominated regimes. Our density-dependent death selection mechanism achieves 97% completion rates but causes fitness decline, revealing a fundamental dilemma where decoupled mechanisms produce bistable dynamics, positively coupled mechanisms create counter-selection pressures, and only deterministic fitness-based selection maintains stability. These findings provide important constraints for future spatial EA design.

Problem

• Spatially Embedded Evolutionary Algorithm
• Population Dynamics
• Embodied Evolution
• Selection Pressure
• Phase Transition

Innovation

• Spatially Embedded Evolutionary Algorithm
• Embodied Evolution
• Spatial Mating
• Population Dynamics
• Phase Transition