Autonomous agents + Genetic algorithms = Fish

This project combines autonomous agents with genetic algorithms to simulate fish behavior. The Shoal repository demonstrates evolutionary simulation where fish agents adapt and evolve through genetic algorithms.

Key Points

• •Combine autonomous agents with genetic algorithms to simulate fish behavior and evolution
• •Use genetic algorithms to evolve agent behaviors over generations, improving fitness traits
• •Implement autonomous agents that make independent decisions based on environmental stimuli
• •Create realistic ecosystem simulations where agents compete, reproduce, and adapt
• •Apply selection pressure through fitness functions to drive behavioral evolution
• •Model fish schooling, predator-prey dynamics, and resource competition in virtual environments
• •Leverage agent-based modeling to understand emergent behaviors from simple individual rules
• •Combine evolutionary computation with multi-agent systems for complex adaptive systems

Workflow Diagram

Concepts