Frédéric Saubion LERIA Learning and Intelligent OptimizatioN Conference Autonomous Search

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Frdric Saubion LERIA Learning and Intelligent OptimizatioN Conference Autonomous Search Slide 2 Based on joint works on this topic with : G. Di Tollo A. Fiahlo Y. Hamadi F. Lardeux J. Maturana E. Monfroy M. Schoenauer M. Sebag Learning and Intelligent OptimizatioN Conference Slide 3 1.Introduction 2.Main Ideas 3.Taxonomy of AS 4.Focus on examples 5.Conclusion and challenges Outline Slide 4 Introduction Generic modeling tools for engineers (Decision) Variables Domains Constraints Mathematical Model Mathematical Model Solver Solving Constraint Optimization and Satifaction Problems Slide 5 Introduction Map coloring problem Satifaction Problems Slide 6 Introduction Map coloring problem Satifaction Problems Slide 7 Introduction Map coloring problem Satifaction Problems Slide 8 Introduction Optimization Problems Travelling Salesman Problem : find a round trip across cities with minimal cost Slide 9 Introduction Optimization Problems Travelling Salesman Problem : find a round trip across cities with minimal cost Slide 10 Introduction Optimization Problems Travelling Salesman Problem : find a round trip across cities with minimal cost Slide 11 Introduction Search landscapes are difficult to explore Many variables Complex constraints Problems are more and more complex Slide 12 Introduction Search landscapes are difficult to explore Exploration vs. Exploitation Balance Problems are more and more complex Slide 13 Introduction An illustrative example : solving SAT SAT CNF instance Devising more and more complex Solving algorithms Litterals Clauses Assignment (1 0 0) Slide 14 Introduction Devising more and more complex Solving algorithms How to explore the binary search space (assignments) to find a solution ? Slide 15 Introduction Devising more and more complex Solving algorithms Use Local Search Slide 16 Introduction Devising more and more complex Solving algorithms Basic Local Search 0 1 0 1 1 Choose a random initial assignment Slide 17 Introduction Devising more and more complex Solving algorithms Basic Local Search Compute the number of true and false clauses Slide 18 Introduction Devising more and more complex Solving algorithms Basic Local Search Try to improve by changing a value (flip) 0 1 0 1 1 0 1 1 1 1 Move to a neighbor Slide 19 Introduction Devising more and more complex Solving algorithms Basic Local Search Until finding a solution Slide 20 Introduction Devising more and more complex Solving algorithms Short overview of the story : a first greedy version GSAT Bart Selman, Hector J. Levesque, David G. Mitchell: A New Method for Solving Hard Satisfiability Problems.AAAI 1992: 440-446 A first boat for binary seas Slide 21 Introduction Devising more and more complex Solving algorithms 1 0 1 0 1 1 0 0 11 11 000 Problem : Many possible moves (many variables) Slide 22 Introduction Devising more and more complex Solving algorithms Restrict neighborhood Select a false clause C abcdefg 0101100 Slide 23 Introduction Devising more and more complex Solving algorithms Get stuck in local optima Slide 24 Introduction Devising more and more complex Solving algorithms Add pertubations Select a false clause C With a random probability p Perform a random flip for C With (1-p) Select the variable with best IMP Perform best move If solution then stop Else go on Parameter ! Slide 25 Introduction Devising more and more complex Solving algorithms Use restarts False Clauses Iterations Parameter ! Slide 26 Introduction Devising more and more complex Solving algorithms WalkSAT : adding a noise and random restart Henry A. Kautz, Bart Selman: Noise Strategies for Improving Local Search..AAAI 1994 Slide 27 Introduction Devising more and more complex Solving algorithms How to break ties ? 0 1 0 1 1 0 0 +3 Slide 28 Introduction Devising more and more complex Solving algorithms Add more sophisticated heuristics Compute the age of the variable If the best variable is not the most recent then flip Else With a random probability p Perform a random flip the second best With (1-p) Flip the best Parameter ! Slide 29 Introduction Devising more and more complex Solving algorithms Novelty : using more strategies to perform improvements (age of the variable) D.A. McAllester, B. Selman and H. Kautz. Evidence for invariant in local search.In Proceedings of AAAI-97, AAAI Press 1997, pages 321-326. Slide 30 Introduction Devising more and more complex Solving algorithms And improvements go on Novelty +,Novelty ++, , TNM, Sattime Slide 31 Introduction Devising more and more complex Solving algorithms Captain Jack : many indicators and thus selection strategies Dave A. D. Tompkins, Adrian Balint, Holger H. Hoos: Captain Jack: New Variable Selection Heuristics in Local Search for SAT. SAT 2011: 302-316 Slide 32 Introduction Adding more parameters and heuristics Devising more and more complex Solving algorithms More flexible algorithms Fit to different instances Set parameters/heuristics values Understand the behavior Slide 33 John Rice. The algorithm selection problem. Technical Report CSD-TR 152, Computer science department, Purdue University, 1975. The Algorithm Selection Problem Main ideas Slide 34 John Rice. The algorithm selection problem. Technical Report CSD-TR 152, Computer science department, Purdue University, 1975. The Algorithm Selection Problem Main ideas Slide 35 Related Questions Main ideas Using several algorithms for solving a class of problems Slide 36 Related Questions Main ideas Adjusting the parameters of one algorithm Slide 37 Main Objectives Main ideas Need for more autonomous solving tools Increasing number of works in this trend : LION, Special sessions in EA conferences (GECCO,) Slide 38 Ideas for More Autonomous Solvers How to use an algorithm that includes Many parameters Many possible heuristics or components Ideas Slide 39 Ideas for More Autonomous Solvers How to use an algorithm that include Many parameters Many possible heuristics or components How to automate all these choices ? Ideas Slide 40 Off-line Automated Tuning Ideas Run your solver on some problems Experiment automatically parameters values Slide 41 Off-line Automated Tuning Ideas Run your solver on new problems with these parameters values Slide 42 Off-line Automated Tuning Ideas Question : Generality of the parameters ? Slide 43 On-line Parameter Control Ideas Try to react during the resolution by changing the parameter Slide 44 On-line Parameter Control Ideas Example : try to increase some parameter when possible Slide 45 On-line Parameter Control Ideas Question : How to react efficiently ? Slide 46 Hyper Heuristics Ideas Combine basic solving heuristics Slide 47 Hyper Heuristics Ideas Get new solvers Slide 48 Hyper Heuristics Ideas Question : How to learn the suitable solver ? Slide 49 Portfolios Based Solvers Ideas Use different types of solvers Slide 50 Portfolios Based Solvers Ideas Learn how to select the right solver for a given problem Slide 51 Portfolios Based Solvers Ideas Question : Reliability of the learning process ? Slide 52 Why introducing the concept of Autonomous Search ? Taxonomy Taxonomies Slide 53 Classification : Solving Solving Methods Tree-Based Search Metaheuristics SLSEA On-line Off-line Auto Complete/incomplete search, Model representation Other optimization paradigms (e.g., ACO ) Taxonomy Slide 54 Classification : Parameters Solving Methods Tree-Based Search SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Numerical/discrete values Components of the solver Vs. Configuration of the solver Taxonomy Slide 55 Classification : Settings Solving Methods Tree-Based Search EA Parameter setting method On-line Off-line Auto Parameter type Experiment-based Feedback Control Measures and learning techniques (reinforcement learning, statistical learning, case- base reasonning) Taxonomy Slide 56 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Parameter Setting in Evolutionary Computation Slide 57 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Parameter Setting Parameter Tuning Parameter Control DeterministicAdaptiveSelf-adaptive Slide 58 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Optimization of algorithms (automated tuning) Slide 59 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Optimization of algorithms (automated tuning) SLS Based (ParamILS) Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Thomas Sttzle: ParamILS: An Automatic Algorithm Configuration Framework. J. Artif. Intell. Res. (JAIR) 36: 267-306 (2009) GA Based (Revac) Volker Nannen, A. E. Eiben: Efficient relevance estimation and value calibration of evolutionary algorithm parameters. IEEE Congress on Evolutionary Computation 2007: 103-110 Racing techniques Mauro Birattari, Thomas Sttzle, Luis Paquete, Klaus Varrentrapp: A Racing Algorithm for Configuring Metaheuristics. GECCO 2002: 11- 18 Slide 60 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Reactive Search Learning for SLS Slide 61 Related Approaches Solving Methods Tree-Based Search Metaheuristics SLSEA Parameter setting method On-line Off-line Auto Parameter type Structural Behavioral Taxonomy Hyper heuristics Slide 62 Hyper Heuristics Taxonomy Two possible views heuristics to choose heuristics heuristics to generate heuristics Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward J Handbook of Meta-heuristic