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name: operators-and-variables description: "Customize pymoo variables, sampling, crossover, mutation, repair, initialization, duplicate elimination, and mixed-variable search workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

operators-and-variables

Use this sub-skill when a task asks how to customize pymoo's search space or evolutionary operators: real/integer/binary/choice variables, mixed-variable problems, permutation or discrete encodings, sampling, crossover, mutation, repair, selection/survival, duplicate elimination, initial populations, or algorithm hyperparameter helper APIs.

Route first

• Objective/constraint _evaluate shapes, G <= 0, bounds warnings, or built-in test problems belong to problem-modeling.
• Running minimize, choosing termination, callbacks, direct stepping, or interpreting Result belongs to optimization-workflows.
• Parallel evaluation, optional joblib/dask/ray, compiled-extension checks, and resource controls belong to performance-and-parallelization.
• Hypervolume, IGD/GD, MCDM, reference directions, decomposition, or plots belong to analysis-and-visualization.
• Stay here for how candidate values are represented and transformed during initialization and variation.

Fast operating checklist

1. Match the encoding to the variables: ordinary real-valued algorithms use float arrays; mixed-variable workflows use vars={...} with Real, Integer, Binary, and Choice objects plus MixedVariableGA or compatible mixed-variable mating.
2. Repair integer/discrete values after float variation: SBX/PM can produce floats unless you use integer-aware variables, RoundingRepair, or custom repair.
3. Respect operator shapes: Sampling._do returns (n_samples, n_var) or an object-compatible list/matrix; Crossover._do receives (n_parents, n_matings, n_var) and returns (n_offsprings, n_matings, n_var); Mutation._do returns mutated X rows.
4. Keep object encodings explicit: for strings, lists, routes, subsets, or dictionaries, use dtype=object, custom duplicate elimination, and problem-specific sampling/crossover/mutation.
5. Initialize deliberately: pass sampling operators, initial arrays, or evaluated populations when the starting design matters.
6. Use repair for feasibility-preserving encodings: bounds repair, rounding, or domain-specific repair can outperform penalty-only constraint handling.
7. Treat optional helper integrations as optional: optuna and comocma are optional extras; do not assume them in a base pymoo environment.

Open the bundled references

• Operator API reference: variable classes, operator base contracts, common import paths, and shape conventions.
• Variable and operator workflows: mixed-variable GA, integer repair, custom object/string operators, initial populations, duplicate elimination, and hyperparameter helper recipes.
• Troubleshooting: fixes for float integers, object dtype mistakes, crossover/mutation shape errors, duplicate handling, invalid initial populations, and missing optional extras.

Bundled script

• scripts/mixed_variable_smoke.py: safe CPU-only smoke that solves a tiny Real/Integer/Binary/Choice problem with MixedVariableGA and asserts valid result types.