Artificial Life evo-edu.org

Steiner-style network design app for systems and optimization exploration.

Application

Network Builder

Use a direct responsive workbench to compare terminal layouts, evolutionary search settings, and the total cost of candidate networks in a Steiner-style optimization problem.

Learner guide Teacher guide Standards and alignment About and provenance
First challengeRandomize terminals, run the default search once, then change only population size or mutation rate and compare the best network cost.
Key habitDiscuss network design as optimization under constraints, not as a claim that real biological systems literally solve Steiner-tree math.
Legacy preservedThe older /apps/steiner/ route remains available as archive and provenance, while this page replaces the iframe wrapper with a direct workbench.

Run Actions

Controls you will use first

Keep terminal-definition, run, report, export, and import actions separate from the search parameters so learners can test one decision at a time.

Current Run Status

Terminals0
Generation0
Best cost---
Manual modeOff

Try This First

Step 1Randomize five terminals and run the search once with the default settings.
Step 2Increase population size and compare whether the best cost falls more smoothly.
Step 3Switch to manual terminal placement and ask how network geometry changes what counts as a good solution.

Main Display

Best network, fitness history, and animation

Keep the best-tree plot, cost history, and animation view visible together. That way the learner can compare geometry, total cost, and generational change without losing context.

What to noticeBetter networks emerge by repeated comparison and variation. They are not created by checking every possible network directly.
Model limitThis is a network-optimization demonstration. It helps discuss efficiency, search, and systems, but it is not a literal simulator of biological evolution or physical infrastructure planning.
Connected pagesRoute OptimizerCumulative Selection ExplorerScientific Virtues

Settings

Search parameters

These values control terminal count, population size, generation depth, and mutation rate for the Steiner-style search.

Report