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Apr 19, 2023 •288 likes •526 views

HIGH-LEVEL SEARCH: FROM HYPER- HEURISTICS TO ALGORITHM SELECTION MUSTAFA MISIR HIGH-LEVEL SEARCH Instead of directly solving a High-level Search Methods given probem (~instance), Operates upon perform a search on algorithm space

  1. HIGH-LEVEL SEARCH: FROM HYPER- HEURISTICS TO ALGORITHM SELECTION MUSTAFA MISIR

  2. HIGH-LEVEL SEARCH • Instead of directly solving a High-level Search Methods given probem (~instance), Operates upon perform a search on algorithm space Low-level Algorithms Operates upon • Generic (Problem- Independent) Methods Potential Solutions

  3. HIGH-LEVEL SEARCH • Can be performed: - ONLINE : while solving a problem instance - OFFLINE : before solving a problem instance

  4. ONLINE

  5. A GENERIC INTELLIGENT Mustafa MISIR Patrick De CAUSMAECKER HYPER-HEURISTIC Greet VANDEN BERGHE Katja VERBEECK

  6. OUTLINE  OBJECTIVE  HYPER-HEURISTICS  A GENERIC INTELLIGENT HH ( GIHH )  COMPUTATIONAL RESULTS

  7. OUTLINE  OBJECTIVE  HYPER-HEURISTICS  A GENERIC INTELLIGENT HH ( GIHH )  COMPUTATIONAL RESULTS

  8. OBJECTIVE Designing a generic problem solver for solving all kinds of search and optimisation problems under different conditions

  9. OUTLINE  OBJECTIVE  HYPER-HEURISTICS  A GENERIC INTELLIGENT HH ( GIHH )  COMPUTATIONAL RESULTS

  10. HYPER-HEURISTICS | DEFINITION Burke et al. (2009) : “A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems”

  11. OUTLINE  OBJECTIVE  HYPER-HEURISTICS  A GENERIC INTELLIGENT HH ( GIHH )  COMPUTATIONAL RESULTS

  14. ADAPTIVE DYNAMIC HEURISTIC SET  Use an elite subset of a given heuristic set  During different parts of a search process (based on the heuristics’ performance )

  18. MOVE ACCEPTANCE + RE-INITIALISATION  Adaptive iterion limited list-based threshold accepting  If doesn’t work, re-initialise

  19. OUTLINE  OBJECTIVE  HYPER-HEURISTICS  A GENERIC INTELLIGENT HH ( GIHH )  COMPUTATIONAL RESULTS

  20. COMPUTATIONAL RESULTS  HyFlex: Hyper-heuristic software framework (Uni. Nottingham)  6 problem domains  Different algorithm (heuristic) sets  GIHH Open source @ http://allserv.kahosl.be/~mustafa.misir/gihh.html

  21. COMPUTATIONAL RESULTS | CHESC 2011 CHeSC 2011 WINNER !  Best algorithm on average across 6 problem domains with a large score margin  Againist 19 competitors

  22. DISCUSSION • A generic high-level search tool • GIHH as the CHeSC 2011 Winner • Robust, easily applicable (generic) • Goal: combine offline and online

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