Results and Design Properties

The completed system demonstrates a stable browser-native evolutionary optimizer whose fitness signal is generated by a real physics engine rather than a hand-written parabola. The model supports target dragging, immediate parameter changes, deterministic seeded generations, replayable best paths, and visible population diversity without requiring a server process.

The main technical result is not that a genetic algorithm can find a projectile velocity; the analytic problem is simpler than that. The result is an inspectable optimization surface where selection pressure, mutation, random resets, field forces, boundary collisions, and solved predicates are all observable in one interface.

• The genome is minimal: two velocity scalars are sufficient to produce rich trajectory behavior.
• The fitness function is discontinuous at target contact, which creates a clear separation between near misses and hits.
• The 75% hit-rate solved predicate avoids declaring success from a single lucky candidate.
• The visual replay layer makes convergence auditable through paths, ghost attempts, hit count, and target distance.