T uning T abu S earch S trategies via V isual D iagnosis

1. TuningTabu Search StrategiesviaVisual Diagnosis >MIC2005<<Vienna< 6th Metaheuristics International Conference August 22-26, 2005. Vienna, Austria By: Lau Hoong Chuin, Wan Wee Chong, and Steven Halim(Presenter)

2. Outline • Introduction (The Problem) • Metaheuristics Tuning Problem (illustrated using Tabu Search) • Visual Diagnosis Tuning (The Methodology) • Human + Computer • {Cause – Action – Outcome} tuple • V-MDF (Visualizer for MDF) (The Tool) • Distance Radar • V-MDF Architecture • Experimental Results • Questions & Answers

3. Tuning Problems Static Dynamic Level-1 CalibratingParameter Values Level-2 ChoosingBest Configuration Level-3 TuningSearch Strategies Introduction: Tuning Problem • Characteristics of a practical metaheuristic: • Delivers high quality solutions for any future instances. • Run in reasonable running time. • Can be developed within tight development time. • The need for a proper tuning. • Taxonomy of Tuning Problem: • Static versus Dynamic • Three levels of complexity • Tuning is complex… (illustrated using Tabu Search)

4. Tuning Problems Static Dynamic Level-1 CalibratingParameter Values Level-2 ChoosingBest Configuration Level-3 TuningSearch Strategies Tuning Tabu Search Level-1 Tuning Problem (Static) (Calibrating Parameter Values) • Setting the length of Tabu tenure: • By Guessing ?? • By Trial and Error ?? • By using past experience as a rough guide ??

5. Tuning Problems Static Dynamic Level-1 CalibratingParameter Values Level-2 ChoosingBest Configuration Level-3 TuningSearch Strategies Tuning Tabu Search Level-2 Tuning Problem (Static) (Choosing the best Configuration) • Choices of Local Neighborhood: • 2-opt ?? • 3-opt ?? • Very Large Scale Neighborhood (VLSN) ?? • Choices of Tabu List: • Tabu moves ?? • Tabu attributes ?? • Tabu solutions ??

6. Tuning Problems Static Dynamic Level-1 CalibratingParameter Values Level-2 ChoosingBest Configuration Level-3 TuningSearch Strategies Tuning Tabu Search Level-3 Tuning Problem (Dynamic) (Tuning Search Strategies) • Choices of Search Strategies: • Intensification ?? • Diversification ?? • Hybridization ?? • Example: • Reactive Tabu Search (Battiti & Tecchiolli, 1994) • When and How to apply these strategies ??

7. Tuning:bottleneck in rapid development process (Adenso-Diaz & Laguna, 2005) Bottleneck !!! Conventional Solution Implement the metaheuristic Evaluate its performance Modify the metaheuristic using pastknowledge, past experiences, plus some instinct blindly.(“Blind trial-and-error”) Good or Give up Not good Stop

8. Automated Tuning Methods Tool to automatically and systematically search for the best: • Set of parameter values (level-1) • Configuration (level-2) Pros: • Relieves the burden of tuning from human. Cons: • Treat metaheuristic as a black box. • Does not provide room for innovations… • Difficult to address level-3 Tuning Problem (for Dynamic Metaheuristic) • Probably slow • if the number of possible configurations is high. Examples: • CALIBRA (Adenso-Diaz & Laguna, 2005) • F-Race (Birattari, 2004)

9. Non-Automated Tuning Methods Tool which allow human to diagnose the metaheuristic Pros: • Make level-3 Tuning Problem for Dynamic Metaheuristic easier • Provide room for innovations… Cons: • Human still need to do the job… • Inconsistent results Examples: • Statistical Analysis, e.g.: • Fitness Landscape Analysis (Fonlupt et al, 1997) • Fitness Distance Correlation Analysis (Merz, 2000) • Human-Guided Tabu Search (Klau et al, 2002) • Visualization of Search (Kadluczka et al, 2004) • V-MDF (This work)

10. Visual Diagnosis TuningThe methodology for solving Tuning Problem V-MDFThe tool to support Visual Diagnosis Tuning

11. Visual Diagnosis Tuning • Idea: Combine human intelligence and computerto produce good search strategies quickly. • Basic methodology of Visual Diagnosis Tuning: • {Cause-Action-Outcome} tuple: • Diagnose incidents in search trajectory. (Cause) • Steer the search if necessary. (Action) • Instantly observe the impact of the his action. (Outcome) • Example: • {passive searching – greedy random restart – arrive in good region} • Possibly an effective strategy • {solution cycling – decrease tabu tenure – solution cycling} • Possibly an ineffective strategy

12. V-MDF: Distance Radar • Diagnose incidents in search trajectory • Visualizing search trajectory is difficult! • Search space is large! • A special generic visualizer is needed: Distance Radar • Using the concept of “distance” • Example:Distance between two binary encoded solutions:hamming distance.A = 110010B = 100011Distance = 2 bit flips.

13. V-MDF: Distance Radar • Main ideas of Distance Radar: • Record elite solutions along the search trajectory. • Distance w.r.t Current solution • Recency w.r.t Current iteration • Objective Value w.r.t Current best objective value • Current solution current position. • Elite solutions (Local Optimal) anchor points. • Approximate Tabu Search trajectory with these information.

14. This is a Recency Graph to augment Radar AX-axis: Local OptimalY-axis: Recency Distance Radarconsists ofRadar A and B.This is Radar A.X-axis: Local OptimalY-axis: Distance Plot distance and recency of these local optimal against current solution and current interation Distance Information in Logarithmicscale Current solution is close to these elite solutions and they are recent.Interpretation: exploring good region In Radar A, elite solutions are sorted by Objective Value V-MDF: Radar A

15. This is an Objective Value Graph to augment Radar BX-axis: Local OptimalY-axis: Objective Value This is Radar B.It portrays distance information from different angle.X-axis: Local OptimalY-axis: Distance Plot distance and obj value of these local optimal against current solution and best so far No cycling, objective value fluctuates. Interpretation: Tabu Search is working correctly at the moment. In Radar B, elite solutions are sorted by Recency V-MDF: Radar B

16. Radar A, B, Recency and Objective Value Graph can be used together to draw more information about the search trajectory V-MDF: Distance Radar

17. For intensification, this is one of the correct trajectory Series of non-improving moves observed…and it requires remedial action For Diversification, this is one of the correct trajectory V-MDF: Remedial Actions

18. Visual Diagnosis Tuning Phase Rules selection phase V-MDF: Overall Architecture Implement the metaheuristic in MDF framework (Lau et al, 2004)See also TSF in Metaheuristics: Progress as Real Problem Solvers. Diagnose the metaheuristic against training instances using Distance Radar Add Rules to Knowledge Base Automatic extraction of Good Rules from Knowledge Baseto form the final metaheuristic algorithm Apply the metaheuristic with good rules to whole test instances

19. Experiment using V-MDF • Task: • Tune a Tabu Search implementation for solving anNP-hard Military Transport Planning (MTP) problem. • Knowledge base of rules after training. • Poor rules are discarded… • Good rules form the final metaheuristic algorithm

20. Experiment using V-MDF • The results of a training instance (minimizing problem)

21. Experiment using V-MDF • Tabu Search results

22. Summary • The Tuning Problem. (The Problem) • Taxonomy of tuning problems: • Static vs Dynamic & 3 levels of complexity. • Current tuning methods: • Automated vs Non-automated • Visual Diagnosis Tuning (The Methodology) • {Cause-Action-Outcome}. • Visualizer for MDF (V-MDF) (The Tool) • Distance Radar and its usage. • Overview of V-MDF • Generic (not restricted to one problem). • Useful especially for new problems.

23. Questions&AnswersThank you for your attention  My e-mail: stevenha@comp.nus.edu.sg