Week 11 · overview
Week 11: Genetic Algorithms and Reinforcement Learning
Genetic algorithms search populations using fitness, selection, crossover, and mutation. Reinforcement learning improves policies through interaction and reward. Both expose the same systems truth: optimization follows the objective you specify, including its omissions and loopholes.
| Day | Role | Evidence |
|---|---|---|
| 1 | Lesson 1: Genetic algorithms | Trace genes, fitness, selection, crossover, and mutation |
| 2 | Lesson 2: Reinforcement learning | Trace environment, state, action, reward, and policy |
| 3 | Investigation | Compare naive and safety-aware objective functions |
| 4 | Lab | Rank supplied behaviors under both objectives |
| 5 | Assessment + Reflection | Repair a highest-score-means-best claim |
Your Objective Function Audit Record preserves candidate evidence, both formulas, score operands, rankings, claim repair, and limitation. The Lab is a deterministic table calculation and controls no physical system.
The complete pinned Microsoft source and credits remain in upstream/.
Review the source record.
Chapter question
How does Genetic Algorithms and Reinforcement Learning change what evidence a student should trust?
What you will understand
You will connect the named mechanism to the evidence it can and cannot support.
What you will do
You will inspect supplied cases, trace the important calculation, and test one bounded claim.
What you will produce
You will produce the week evidence record named in the Lab or reflection.
How the week connects
This week carries the previous AI mechanism forward into a stricter evidence check.
Key vocabulary
mechanism; evidence; boundary; test; claim
Approximate time
Five class meetings at about 210 instructional minutes total.
Final evidence required
Submit the completed week record with at least two exact labels from the artifact.