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For Researchers · Genetic Algorithm

Genetic Algorithm for a percolation cluster

Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a percolation cluster live below — adjust the inputs and watch it respond, right in your browser.

Genetic AlgorithmLive

evolution as optimization

Controls

Speed1×

Presets

Population

Mutation rate

A population of candidate solutions is scored by fitness (bright = high). The fittest are selected, crossed over, and mutated each generation. Watch the swarm climb toward the global peak while dodging local optima.

▶ Run in Python

Data Inspector

Generation0

Best fitness0.000

Population80

Governing equation

P_{i} = \frac{f _{i}}{\sum _{j} f _{j}}, μ = 0.08, f_{m a x} = 0.000

Reading this result: Balanced mutation (0.08) with population 80: enough diversity to escape local optima while still converging — the classic explore-vs-exploit sweet spot.

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Frequently asked questions

Is this good for researchers?: Yes — this version of "Genetic Algorithm for a percolation cluster" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.