Implement scheduling algorithms and experimentation framework #3
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algorithms/multi_objective/nsga2.py
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| def repair(child: List[int], segment: List[int], donor: List[int]) -> None: | ||
| mapping = {donor[i]: segment[i] for i in range(cx_point1, cx_point2)} | ||
| for idx in list(range(cx_point1)) + list(range(cx_point2, size)): | ||
| while child[idx] in mapping: | ||
| child[idx] = mapping[child[idx]] |
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The PMX crossover’s repair step maps donor genes to the swapped segment (mapping = {donor[i]: segment[i]}) and then repeatedly replaces child[idx] while that value is in the mapping. When a value from the inserted segment also occurs outside the crossover window—which is common for permutations—the key for that value is missing because the mapping is reversed. The loop therefore never replaces the duplicate (or can oscillate between two keys), yielding children with repeated or missing job indices and potentially non‑terminating while loops. Invalid permutations break the subsequent scheduling evaluation and can stall the NSGA‑II search.
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Summary
Testing
https://chatgpt.com/codex/tasks/task_e_6905635dcc148324aab009e512efab09