Module nsga2 

NSGA-II: Non-dominated Sorting Genetic Algorithm II

Implements the landmark algorithm by Deb et al. (2002) for multi-objective optimisation. Key ideas:

1. Fast non-dominated sorting — assign each individual a Pareto-front rank in O(MN²) time.
2. Crowding-distance assignment — within each front, measure how isolated each point is, rewarding diversity.
3. Tournament selection — prefer lower rank; break ties by crowding distance (larger is better).
4. Simulated Binary Crossover (SBX) and Polynomial Mutation (PM) for real-valued variables.
5. Elitist survivor selection — combine parent and offspring populations (size 2N) and select the best N individuals.

References

• Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

Structs

Individual

An individual in the NSGA-II population.

Nsga2Config

Configuration for the NSGA-II algorithm.

Nsga2Result

Result returned by nsga2.

Functions

nsga2

Run NSGA-II on a multi-objective optimisation problem.