- optim/search_space.py: Genome encodes only indices into the real
component database plus continuous/discrete geometry parameters; number
of stages is itself mutable via add/remove/duplicate-stage operators.
repair() clips everything back into bounds after mutation/crossover so
decode() never sees an invalid genome.
- optim/objective.py: evaluate() decodes a genome, computes real BOM cost
from wire length/price and component prices, runs the full chain, and
scores fitness = efficiency for feasible runs or a soft penalty scaled
by how many stages it got through for infeasible ones (so the GA gets
gradient instead of a wall).
Tested against the real component database (not synthetic fixtures) —
including an explicit infeasible case using the real inductive sensor's
actual sensitivity/threshold values.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
