Question 1

How do I split a list of names into random groups?

Accepted Answer

Paste your names (one per line) into the text box, set the number of groups or people per group, and click "Generate Groups". Your groups appear instantly.

Question 2

Is GroupMixer free?

Accepted Answer

Yes. GroupMixer is completely free to use. If you find it useful and want to support development, you can donate through GitHub Sponsors.

Question 3

Are there any usage limits?

Accepted Answer

No. There are no usage limits on GroupMixer.

Question 4

Do I need an account?

Accepted Answer

No. You do not need an account or sign-up. Paste names, adjust the setup, and generate groups right away.

Question 5

Where is my data processed?

Accepted Answer

In your browser on this device. Your participant list and all other data you enter stays on your device while you work.

Question 6

Does it work offline after first load?

Accepted Answer

Yes. After the page has loaded, you can keep using GroupMixer offline in your browser.

Question 7

Can I keep people together or apart?

Accepted Answer

Yes. Open the advanced options to add Keep Together and Keep Apart rules, multiple sessions, and attribute balancing.

Question 8

Can I balance by role, skill, gender, or department?

Accepted Answer

Yes. Add attributes such as role, skill, gender, or department to your participants, then choose which attribute to balance across groups.

Question 9

Can I fix certain people to groups?

Accepted Answer

Yes. Use Pinned people in the advanced options to pin specific people to a specific group across all sessions. This is useful for leaders, presenters, or anyone who must stay in a known group.

Question 10

Can I avoid repeat pairings across rounds?

Accepted Answer

Yes. Set the number of sessions and enable "Avoid repeat pairings" to cut down on repeats across rounds.

Question 11

How can I create Zoom breakout rooms with minimal repeats?

Accepted Answer

Import a Zoom pre-assign CSV or a spreadsheet with participant emails, choose the number of rooms and sessions, keep "Minimize repeat pairings" enabled, then generate groups. Use Share & Export → Download Zoom breakout CSV. For multiple sessions, GroupMixer downloads a ZIP with one Zoom CSV per round; upload the right CSV in Zoom’s Breakout Room pre-assign screen before that round. Zoom matches people by email address, so make sure participants join with the same email.

Question 12

Can I create groups for people who do not attend every session?

Accepted Answer

Yes. Use the scenario editor for partial attendance. You can set which participants attend each session before generating the schedule.

Question 13

Can I set different group sizes or room capacities?

Accepted Answer

Yes. Use the scenario editor to set custom group capacities, including different capacities for different groups or sessions.

Question 14

Can I control how often people visit specific groups?

Accepted Answer

Yes. Use Group Visits in the scenario editor to set exact, minimum, or maximum visit counts for selected people and selected groups across selected sessions. This is useful for station rotations, task exposure, and visit caps.

Question 15

When should I use the scenario editor?

Accepted Answer

Use the scenario editor for controls this page does not expose, such as partial attendance, custom capacities by group and session, session-specific rules, weighted preferences, Encounters, Group Visits, advanced tuning, solver settings, previous results, and result analysis.

Question 16

Is GroupMixer a random group generator?

Accepted Answer

Yes, but it does more than shuffle names. Each browser run obtains a fresh seed from the browser’s cryptographically secure randomness API. That seed drives randomized exploration in the local Rust and WebAssembly solver. Group sizes, requirements, balancing, repeat reduction, and other selected goals shape which valid schedule is returned, so GroupMixer does not claim to sample uniformly from every possible grouping.

Question 17

What algorithm does GroupMixer use?

Accepted Answer

GroupMixer uses a hybrid optimization pipeline. It compiles the scenario into a dense indexed representation, builds a high-quality feasible starting schedule, then runs a local-search optimizer with score-preserving move previews, record-to-record acceptance, adaptive move-family selection, diversification, and runtime-scaled stopping.

Question 18

What is the objective function?

Accepted Answer

The solver minimizes a weighted score. The main terms include the Avoid Repeats objective, attribute-balance penalties, weighted preferences, and the Maximize Contacts reward. Lower is better for the same configuration.

Question 19

How does the solver create its initial schedule?

Accepted Answer

GroupMixer does not start from a purely random grouping. It first tries a bounded constraint-scenario/oracle-guided constructor. That constructor builds a hard-valid scaffold, uses pure Social Golfer structure where it helps reduce repeats, projects that structure back into the real scenario, and validates the result before search begins.

Question 20

What happens after the starting schedule is built?

Accepted Answer

The dense search engine repeatedly proposes local changes to the current schedule. Each candidate change is previewed against the scoring model before the optimizer decides whether to keep it, so the search can improve the schedule while preserving hard validity.

Question 21

Does GroupMixer use metaheuristics for search?

Accepted Answer

Yes. The main search is an iterated local-search style optimizer with record-to-record or threshold-accepting behavior: improving moves are accepted, and bounded non-improving moves can be accepted early so the search can leave local optima. GroupMixer also uses adaptive neighborhood selection, diversification bursts, and tabu-inspired machinery in specialized repeat-minimization paths. It is not just one textbook metaheuristic; it combines several search ideas around the group-assignment scoring model.

Question 22

How does GroupMixer avoid repeated pairings?

Accepted Answer

Repeat reduction starts before local search. GroupMixer tries to construct a strong social-golfer-style contact pattern, using exact or near-exact zero-repeat structures when they match the scenario. After that, dense search tracks pair-contact counts, rewards first-time contacts, penalizes repeat excess, and improves the schedule without breaking requirements.

Question 23

How are requirements and preferences handled?

Accepted Answer

Requirements such as attendance, capacity, Fixed Placements, Keep Together, and Keep Apart are enforced during schedule construction and validated before search. Preferences such as Balance Attributes, Attribute Totals, Prefer Together, Prefer Apart, Encounters, and Group Visits are scoped weighted targets. Avoid Repeats and Maximize Contacts are global objectives.

Question 24

What is the difference between requirements and preferences?

Accepted Answer

Requirements are rules GroupMixer must satisfy to return a schedule. Preferences are scoped weighted targets involving selected people, groups, or sessions. Objectives such as Avoid Repeats and Maximize Contacts measure global schedule quality.

Question 25

Can GroupMixer prove global optimality?

Accepted Answer

If GroupMixer finds a score of 0, that indicates optimality for the active scoring model: there are no remaining scored violations or repeat penalties to improve. For many constrained scenarios, however, the true optimum may be greater than 0 because the requirements conflict or the design is mathematically impossible. GroupMixer is not designed to prove that a positive score is globally optimal, and for many complex scenarios that proof is completely infeasible in practice.

Question 26

Are there other tools that can find better schedules than GroupMixer?

Accepted Answer

In most cases, probably not. General optimization tools such as OR-Tools can model parts of this problem, but for most practical scenarios it is exceedingly hard to get better schedules than GroupMixer by using a generic solver alone. GroupMixer combines search with problem-specific construction heuristics that generic constraint search will not rediscover cheaply: round-robin pair schedules, Kirkman and nearly Kirkman triple systems, finite-field affine-plane and transversal-design constructions, MOLS and MOLR constructions, quasi-difference-matrix RTDs, resolvable BIBDs, own-social-golfer starter banks, published schedule banks, and recursive lifting. Beating GroupMixer usually requires another combined algorithm, not just a generic model.

Question 27

What should I do if I find a better schedule?

Accepted Answer

Please report it through the feature request page. Include the scenario shape, the GroupMixer result, the better schedule, and how you scored it. Cases where another method beats GroupMixer are especially useful because they can become regression tests or benchmark cases for improving the solver.

Question 28

Why can Generate or Reshuffle produce different groups?

Accepted Answer

Generate and Reshuffle each start a new solve, so the browser supplies a fresh secure random seed. Different seeds can lead the optimizer to different high-quality schedules with the same or similar scores. In the scenario editor, a deterministic seed controls the randomized sequence. Exact reproduction also requires the same inputs, settings, solver version, and deterministic stopping conditions; time-limited runs can stop at different points.

Question 29

Does GroupMixer silently ignore difficult rules?

Accepted Answer

No. Requirements are treated as feasibility rules, not suggestions. GroupMixer first tries quality-oriented constructors; if those cannot build a schedule, it can fall back to a SAT-based feasibility constructor. If the SAT solver proves the requirements unsatisfiable, GroupMixer fails instead of relaxing them. If a satisfying schedule is found, GroupMixer uses it as the starting point and continues optimizing preferences and other quality objectives.

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GroupMixer has a free public API.

Frequently asked questions

Using GroupMixer

Privacy, access, and limits

Controls and advanced setup

Randomness and reproducibility

Auto algorithm

Search and repeat reduction

Constraints and optimality

Benchmarks and better results