Teacher Guide

Route Optimizer teacher guide

Use this guide to frame Route Optimizer as a model of heuristic search and iterative improvement, not as a black-box route oracle.

Open application Learner guide Standards and alignment

Lesson Framing

Teacher emphasis

Position the app as an example of heuristic search in a huge solution space.
Have learners compare runs rather than relying on one dramatic result.
Keep model limitations visible: this is route optimization, not a biological simulation.

Suggested sequence

Run the default city set once.
Change one search parameter at a time.
Discuss why a search strategy may outperform naive route guessing without proving optimality.

Discussion Moves

Questions to ask

What changed when the population size increased?
What kind of tradeoff appears when selection pressure becomes stronger or weaker?
How do repeated runs help you judge whether a search setting is robust?

Model-limit reminders

The app uses a genetic-algorithm style search strategy, not an exhaustive proof of shortest-path optimality.
Manual city placement is useful for thought experiments, but click accuracy is imperfect.
Use results as evidence about search behavior, not as a claim that evolution literally solves route maps.