Nsga refactor #16
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Nsga refactor #16
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danyoungday
commented
Mar 10, 2025
| S = [[] for _ in range(population_size)] # Set of solutions dominated by p | ||
| n = [0 for _ in range(population_size)] # Number of solutions dominating p | ||
| front = [[]] # Final Pareto fronts | ||
| rank = [0 for _ in range(population_size)] # Rank of solution |
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Better documentation
danyoungday
commented
Mar 10, 2025
| # If p dominates q | ||
| if dominates(population[p], population[q]): | ||
| if q not in S[p]: | ||
| S[p].append(q) |
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This wasn't needed since we don't have duplicates in the population. Not sure why it was here in the first place
danyoungday
commented
Mar 10, 2025
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| # With this implementation the final front will be empty | ||
| del front[len(front)-1] | ||
| front.pop() |
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This is a nicer way to do this I think
danyoungday
commented
Mar 10, 2025
| if obj_max != obj_min: | ||
| for i in range(1, len(sorted_front) - 1): | ||
| dist = sorted_front[i+1].metrics[m] - sorted_front[i-1].metrics[m] | ||
| # Normalize distance by objective range |
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More documentation for the distance function
danyoungday
commented
Mar 10, 2025
| true_ranks = [1, 1, 1, 2, 3, 3, 3, 4, 4, 4] | ||
| obj1 = [0.15, 0.05, 0.02, 0.18, 0.83, 0.30, 0.43, 0.37, 0.73, 0.60] | ||
| obj2 = [0.15, 0.86, 0.96, 0.18, 0.21, 0.52, 0.29, 0.95, 0.59, 0.70] | ||
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Cleaned up the
fast_non_dominated_sortfunction and added a unit test to test it.