Research map / Learning theory and algorithms / Combinatorial and search optimization
A Combinatorial Search Perspective on Diverse Solution Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace exhaustive combination exploration (ACER) with most-promising seed exploration (MCER, n=30) (ablation) | DFA-based diverse plan search · Blocks P.no. 8-30-0.5 | time to find K-set (sec): 32.90 -> 0.58 | Helped |
| replace ACER with MCER (ablation) | A*A-based diverse plan search · Blocks P.no. 8-30-0.5 | time to find K-set (sec): 11.06 -> 0.04 | Helped |
| replace ACER with MCER (ablation) | DFA-based search · Blocks P.no. 5-0, K=8, min dist 0.5 | largest diverse set size in 60s: 4 -> 6 | Helped |
| replace ACER with MCER (ablation) | DFA-based search · Blocks P.no. 5-0, K=8, min dist 0.6 | largest diverse set size in 60s: 3 -> 2 | Hurt |
| replace ACER with MCER (ablation) | DFA-based search · Rovers roverprob4213, K=8, min dist 0.5 | K-set found within 60s: No* (6) -> 9.84 sec | Helped |
| replace ACER with MCER (ablation) | DFA-based search · Zeno-Travel ZTRAVEL-2-5, K=8, min dist 0.6 | K-set found within 60s: No* (3) -> 21.46 sec | Helped |
| replace greedy exploration with MCER | DFA-based search · Rovers roverprob4213, K=8, min dist 0.6 | largest diverse set size in 60s: 4 -> 6 | Helped |
| replace greedy exploration with MCER | DFA-based search · Rovers roverprob4213, K=8, min dist 0.5 | time to find K-set (sec): 1.22 -> 9.84 | Hurt |
| replace greedy exploration with MCER | DFA-based search · Blocks P.no. 8-30-0.5 | K-set found: No (6) -> Yes (0.58 sec) | Helped |
| use dispersion-based heuristics instead of trivial 0 heuristic in MCER (ablation) | MCER · planning domains | diverse set search performance | No clear effect |
Closest papers by meaning
- A Framework to Design Approximation Algorithms for Finding Diverse Solutions in Combinatorial Problems (AAAI 2023 · similarity 0.91)
- Reshaping Diverse Planning (AAAI 2020 · similarity 0.89)
- PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization (ICLR 2025 · similarity 0.86)
- HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMs (AAAI 2025 · similarity 0.85)
- Theoretical Aspects of Generating Instances with Unique Solutions: Pre-assignment Models for Unique Vertex Cover (AAAI 2024 · similarity 0.85)
- Large Language Models as End-to-end Combinatorial Optimization Solvers (NeurIPS 2025 · similarity 0.85)
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