Summary of Search Strategy Generation For Branch and Bound Using Genetic Programming, by Gwen Maudet et al.
Search Strategy Generation for Branch and Bound Using Genetic Programming
by Gwen Maudet, Grégoire Danoy
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces GP2S, a machine learning approach that generates a Branch-and-Bound (B&B) search strategy heuristic using genetic programming. The goal is to make intelligent decisions while being computationally lightweight. A policy function evaluates B&B node quality by combining features from the node and problem, and the search strategy policy is defined as best-first search based on this ranking. The approach is evaluated on primal hard problems, achieving an average speedup of 11.3% compared to the SCIP solver, with some methods outperforming SCIP in terms of feasible solutions or optimality gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GP2S is a new way for computers to solve complex math problems. It uses machine learning to find the best order to explore a huge search space. This approach is called Branch-and-Bound (B&B). The goal is to make decisions quickly and correctly, even on very hard problems. GP2S outperforms other methods, solving more problems correctly and faster. |
Keywords
» Artificial intelligence » Machine learning
