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Module 22: Constrained & Combinatorial EC - Real Problems Have Rules

Real optimization has constraints: budgets, physics, regulations. And many problems are combinatorial: schedules, routes, assignments. EC handles both through clever representations, repair operators, and constraint-handling techniques.

Learning Objectives​

  1. Implement penalty functions for constrained optimization
  2. Understand Deb's feasibility rules and epsilon-constraint method
  3. Code repair operators for infeasible solutions
  4. Solve scheduling and bin-packing with EC
  5. Handle mixed-integer optimization with evolutionary methods

Concept Explanation​

Coming soon.

Code Examples​

Coming soon.

Exercises​

Coming soon.

Milestone Checklist​

  • Solved a constrained problem with penalty methods
  • Implemented Deb's feasibility rules
  • Built a GA for a scheduling problem
  • Understand decoder approaches

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