Name
Abstract | ||
|---|---|---|
We propose a framework which we call stochastic off-line programming (SOP). The idea is to embed the development of combinatorial algorithms in an off-line learning environment which helps the developer choose heuristic advisors that guide the search for satisfying or optimal solutions. In particular, we consider the case where the developer has several heuristic advisors available. Rather than selecting a single heuristics, we propose that one of the heuristics is chosen randomly whenever the heuristic guidance is sought. The task of SOP is to learn favorable instance-specific distributions of the heuristic advisors in order to boost the average-case performance of the resulting combinatorial algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICTAI.2009.23 | International Journal on Artificial Intelligence Tools |
Keywords | DocType | Volume |
stochastic offline programming,heuristic guidance,optimal solution,stochastic off-line programming,heuristic advisor,single heuristics,off-line learning environment,average-case performance,combinatorial algorithm,favorable instance-specific distribution,stochastic programming,benchmark testing,greedy algorithms,programming,tuning,construction industry,satisfiability,data mining | Conference | 19 |
Issue | ISSN | ISBN |
4 | 1082-3409 E-ISBN : 978-0-7695-3920-1 | 978-0-7695-3920-1 |
Citations | PageRank | References |
3 | 0.40 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuri Malitsky | 1 | 278 | 17.79 |
| Meinolf Sellmann | 2 | 728 | 48.77 |