Tradeoffs and Limits

The implementation favors inspectability over raw optimization performance. Rebuilding a Matter.js world for every genome is more expensive than deriving a projectile equation or pooling physics objects, but it makes evaluation isolated: a candidate cannot inherit velocity, collision state, or sleeping flags from a previous candidate. That isolation is valuable when the simulation itself is the fitness oracle.

The genome is also intentionally tiny. It contains only the initial velocity, so the system cannot learn mid-flight steering, spin control, or multi-action strategies. That limitation keeps the search space understandable: every visible behavior is the result of two numbers interacting with gravity, wind, walls, restitution, and target placement.

• A physics-engine rollout is slower than an analytic projectile solver, but it makes collisions and field changes natural.
• A two-scalar genome is easy to visualize, but it cannot express active control after launch.
• A strong hit bonus accelerates convergence after contact, but it makes the fitness landscape sharply discontinuous.
• Deterministic seeds make behavior reproducible, while reroll and drag controls keep the demo exploratory.