I’m thrilled to share findings from my latest research project: Evolving Organisms 2026. This project explores how complex, adaptive behavior emerges in artificial life through sophisticated neuroevolutionary techniques.

Building upon foundational work in the field, this project moves beyond simple weight optimization to explore dynamic brain topologies and adversarial ecosystems.

The Research Roadmap

The project followed a rigorous five-phase research cycle:

1. Baseline Evolution

We started with simple neuroevolution, training agents for basic food gathering tasks. This established a performance baseline for comparing more advanced techniques.

2. Sensory Expansion

We significantly upgraded the agents’ perception systems:

• 32-Ray Vision: Allowing high-fidelity environmental scanning.
• Proximity Sensors: For close-range interaction.
• Internal State Monitoring: Giving agents awareness of their own energy and health levels.

3. Structural Growth (NEAT)

Instead of training neural networks with fixed structures, we implemented NeuroEvolution of Augmenting Topologies (NEAT). This allows the agents’ brains to grow new neurons and connections over time, starting minimal and complexifying only as needed.

Key Finding: NEAT-evolved organisms outperformed fixed-topology networks by 42% in navigation tasks, demonstrating that structural efficiency is just as critical as weight optimization.

4. Quality Diversity (MAP-Elites)

To prevent premature convergence (where all agents just copy one “good enough” strategy), we used MAP-Elites. This algorithm maintains a “behavioral atlas” of diverse strategies (e.g., organisms that are Fast & Exploratory vs. Slow & Efficient).

Result: This successfully prevented “speciation collapse” and maintained a healthy variety of survival strategies.

5. Adversarial Co-evolution

The final phase introduced a dynamic predator-prey ecosystem.

• Prey: Evolved to gather food and avoid predators.
• Predators: Evolved to catch prey.

Emergent Behavior: Within just 15 generations of predator introduction, prey species evolved distinct “zigzag” evasion patterns — a strategy that was never explicitly programmed but emerged entirely through survival pressure.

Conclusion

This research demonstrates that robust artificial life thrives in environments that challenge them both physically (via complex sensors) and socially (via adversarial dynamics). Treating evolution as a structural and behavioral problem yields far more adaptive agents than simple parameter tuning.

Check out the full code and research logs on GitHub: phon3/evolving-organisms-2026