Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining
| Autor: | Jimmy H. M. Lee, Hongbo Li, He Mi, Minghao Yin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Mathematical optimization Computer science 05 social sciences 0202 electrical engineering electronic engineering information engineering Constrained optimization Benchmark (computing) 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences 02 engineering and technology General Medicine Search tree Constraint optimization problem |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v34i02.5518 |
| Popis: | Making good decisions at the top of a search tree is important for finding good solutions early in constraint optimization. In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems. We demonstrate that applying FPM in a small number of random high-quality feasible solutions enables us to identify subtrees containing optimal solutions in more than 55% of problem instances for four real world benchmark problems. The method works as a plugin that can be combined with any search strategy for branch-and-bound search. Exploring the identified subtrees first, the method brings substantial improvements for four efficient search strategies in both total runtime and runtime of finding optimal solutions. |
| Databáze: | OpenAIRE |
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