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Selection, Crossover, and Mutation

Look up syntax, contracts, layouts, algorithms, and exact behavior.

After evaluation, attempts are sorted by descending fitness. A configurable elite share is copied directly into the next generation. Parents are chosen from the top portion of the ranked population using a rank-biased picker, so stronger attempts are more likely to breed without making the population entirely deterministic.

gc=λga+(1−λ)gb,λ∈[0.28,0.72]g_c = \lambda g_a + (1-\lambda)g_b, \quad \lambda \in [0.28, 0.72]
Blend crossover. Child velocity is a weighted blend of two parent launch velocities.

Children are generated by blending parent velocities, then applying random jitter with probability equal to the mutation rate. A small random-reset group is added each generation to preserve exploration when the current population converges too narrowly.

The algorithm therefore balances three kinds of memory. Elites preserve what is already known to work, crossover searches between plausible velocities, and resets admit that the whole current family may be wrong after the target moves or the wind changes. The population behaves like a set of guesses that remembers winners but keeps a few wildcards alive.

gc′=gc+(ϵx,ϵy)g'_c = g_c + (\epsilon_x, \epsilon_y)
Mutation. When mutation fires, velocity jitter is scaled by the current wind and gravity settings.
  • Elite share defaults to 18% and is clamped to preserve at least two elites.
  • The parent pool includes at least the elite set plus extra high-ranking attempts.
  • Random resets occupy about 8% of the population.
  • Velocity bounds keep candidate launches inside a useful range.

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