Application

Route Optimizer

Use a direct responsive workbench to compare route quality, population settings, and iterative improvement in a genetic-algorithm style Traveling Salesperson search.

Learner guide Teacher guide Standards and alignment About and provenance

First challengeGenerate a route set, run the solver once, then change just one search parameter and compare the best distance trend.

Key habitUse the app to discuss search and optimization, but keep its algorithmic assumptions and limits visible rather than treating it as a magic route finder.

Legacy preservedThe older TSP route remains available as an archive, while this page replaces the iframe wrapper with a direct workbench.

Run Actions

Controls you will use first

Keep generate, start, stop, save, export, and import actions separate from population and selection settings so you can compare one search decision at a time.

Import Settings

Current Run Status

Generation0

Best distance0

Number of cities0

Population0

Try This First

Step 1Generate a random set of cities and run the default search once.

Step 2Raise or lower population size, rerun, and compare how quickly the best distance stabilizes.

Step 3Switch to manual city definition and ask how city placement changes what counts as a good search path.

Main Display

Route surface and search history

Keep the route canvas and the history plots visible together. That way the learner can compare geometric change, best-distance improvement, and generation count without bouncing between disconnected views.

What to noticeBetter routes usually emerge through repeated comparison and variation, not through checking every possible path directly.

Model limitThis is an algorithmic optimization demonstration. It is useful for talking about search heuristics and iterative improvement, not as a model of real ecological movement or literal natural selection.

Connected pagesCumulative Selection Explorer Network Builder Scientific Virtues

Settings

Search parameters

These values control route size, the evolving population, selection pressure, and the share of routes receiving 2-opt improvement.