Research map / Learning theory and algorithms / Submodular and game algorithms
An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace generalized greedy with EAMC (evolutionary algorithm on surrogate objective) | subset selection with cost constraints · max coverage, influence maximization, sensor placement | objective value f | Helped |
| replace POMC with EAMC | subset selection with cost constraints · influence maximization graph200 out-degree | objective value f | Mixed |
| compare new solution only within same-size bins and keep two solutions per bin (EAMC) rather than a Pareto population (POMC) (ablation) | EA for subset selection · influence maximization graph200 out-degree | population size: 70 -> 23 | Helped |
| use noisy objective evaluation (influence spread simulations) with EAMC | EAMC · influence maximization | influence spread over time | Hurt |
Closest papers by meaning
- Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint (ICML 2025 · similarity 0.93)
- Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints (AAAI 2021 · similarity 0.86)
- Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization (ICML 2024 · similarity 0.83)
- Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination (ICML 2025 · similarity 0.83)
- Towards Running Time Analysis of Interactive Multi-Objective Evolutionary Algorithms (AAAI 2024 · similarity 0.82)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.82)
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