Add user-tunable objective_weights and Dirichlet sampling to ParEGO#1305
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JannisKst wants to merge 3 commits intodevelopmentfrom
Open
Add user-tunable objective_weights and Dirichlet sampling to ParEGO#1305JannisKst wants to merge 3 commits intodevelopmentfrom
JannisKst wants to merge 3 commits intodevelopmentfrom
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…or guided multi-objective search
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Hey, I'm happy you guys are working on this feature I was asking for a year ago in #1235. Will you make a release containing this feature? |
added 2 commits
April 7, 2026 11:32
- Moved the `objective_weights` parameter from multi-objective algorithms (MeanAggregationStrategy, ParEGO) to the Scenario. - Updated MeanAggregationStrategy and ParEGO to read weights from the scenario. - Refactored tests and Facades to accommodate the new location of objective_weights.
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This PR introduces an objective_weights parameter to ParEGO, allowing users to bias the multi-objective search toward preferred regions of the Pareto front. Weights are validated, normalized, and used to sample scalarization weights via a Dirichlet distribution, controlled by concentration_scale.
If no weights are provided, ParEGO defaults to uniform random scalarization.
Possible consideration:
The handling of objective_weights is currently implemented within ParEGO and MeanAggregationStrategy. It may be beneficial to centralize this logic, either in the Scenario or in AbstractMultiObjectiveAlgorithm to reduce duplication and ensure consistent behavior across strategies.