Module 16: Multi-Objective Evolutionary Optimization - When One Objective Isn't Enough
Real problems have trade-offs: cost vs quality, speed vs accuracy, risk vs reward. Multi-objective EA finds the entire Pareto front of non-dominated solutions, giving decision-makers a menu of optimal trade-offs.
Learning Objectives
- Understand Pareto dominance and Pareto fronts
- Implement non-dominated sorting
- Code NSGA-II (crowding distance, fast non-dominated sort)
- Implement SPEA2 (strength Pareto)
- Use pymoo for production multi-objective optimization
Concept Explanation
Coming soon.
Code Examples
Coming soon.
Exercises
Coming soon.
Milestone Checklist
- Built NSGA-II from scratch
- Visualized Pareto fronts on ZDT benchmarks
- Understand crowding distance
- Used pymoo on a bi-objective problem
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