Skip to main content

Abstract

Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand. In this chapter, we propose a general definition and a taxonomy of search processes w.r.t. their computation characteristics. This formalism is expressed by some computation rules between computation states. The sequence of application of these rules (i.e., the strategy) then characterizes the search process itself. Using these rules we then classify and compare well-known solvers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. D. Applegate, R. Bixby, V. Chvatal, W. Cook, The Traveling Salesman Problem: A Computational Study. Princeton Series in Applied Mathematics (Princeton University Press, Princeton, 2007)

    Google Scholar 

  2. A. Arbelaez, Y. Hamadi, Exploiting weak dependencies in tree-based search, in ACM Symposium on Applied Computing (SAC), Honolulu, Hawaii, USA (ACM, New York, 2009), pp. 1385–1391

    Google Scholar 

  3. A. Arbelaez, Y. Hamadi, M. Sebag, Continuous search in constraint programming, in 22nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2010 (IEEE Comput. Soc., Los Alamitos, 2010), pp. 53–60

    Chapter  Google Scholar 

  4. E. Aarts, J.K. Lenstra (eds.), Local Search in Combinatorial Optimization (Princeton University Press, Princeton, 2003)

    MATH  Google Scholar 

  5. K. Apt, Principles of Constraint Programming (Cambridge University Press, Cambridge, 2003)

    Book  MATH  Google Scholar 

  6. R. Battiti, M. Brunato, Reactive search optimization: learning while optimizing, in Handbook of Metaheuristics, 2nd edn., ed. by M. Gendreau, J.Y. Potvin (Springer, Berlin, 2009)

    Google Scholar 

  7. R. Battiti, M. Brunato, F. Mascia, Reactive search and intelligent optimization. Technical Report, Dipartimento di Informatica e Telecomunicazioni, Universita di Trento, Italy (2007)

    Google Scholar 

  8. R. Battiti, M. Brunato, F. Mascia, Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces, vol. 45 (Springer, Berlin, 2008)

    MATH  Google Scholar 

  9. M. Bader-El-Den, R. Poli, Generating SAT local-search heuristics using a GP hyper-heuristic framework, artificial evolution, in 8th International Conference, Evolution Artificielle, EA 2007. Revised Selected Papers. Lecture Notes in Computer Science, vol. 4926 (Springer, Berlin, 2008), pp. 37–49

    Google Scholar 

  10. F. Benhamou, L. Granvilliers, Continuous and interval constraints, in Handbook of Constraint Programming, ed. by F. Rossi, P. van Beek, T. Walsh (Elsevier, Amsterdam, 2006). Chapter 16

    Google Scholar 

  11. E.K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, R. Qu, A survey of hyper-heuristics. Technical Report NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham (2009)

    Google Scholar 

  12. E.K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, J. Woodward, A classification of hyper-heuristics approaches, in Handbook of Metaheuristics, 2nd edn., ed. by M. Gendreau, J.Y. Potvin (Springer, Berlin, 2009)

    Google Scholar 

  13. F. Boussemart, F. Hemery, C. Lecoutre, L. Sais, Boosting systematic search by weighting constraints, in Proceedings of the 16th European Conference on Artificial Intelligence, Valencia, Spain, ed. by R.L. de Mántaras, L. Saitta (IOS Press, Amsterdam, 2004), pp. 146–150

    Google Scholar 

  14. A. Biere, M.J.H. Heule, H. van Maaren, T. Walsh (eds.), Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185 (IOS Press, Amsterdam, 2009)

    MATH  Google Scholar 

  15. L. Bordeaux, Y. Hamadi, L. Zhang, Propositional satisfiability and constraint programming: a comparative survey. ACM Comput. Surv. 9(2), 135–196 (2006)

    Google Scholar 

  16. E.K. Burke, G. Kendall, J. Newall, E. Hart, P. Ross, S. Schulenburg, Hyper-heuristics: an emerging direction in modern search technology, in Handbook of Metaheuristics, ed. by F. Glover, G. Kochenberger (Kluwer Academic, Dordrecht, 2003), pp. 457–474

    Google Scholar 

  17. J. Boyan, A. Moore, P. Kaelbling, Learning evaluation functions to improve optimization by local search. J. Mach. Learn. Res. 1, 77–112 (2000)

    Google Scholar 

  18. M. Birattari, T. Stützle, L. Paquete, K. Varrentrapp, A racing algorithm for configuring metaheuristics, in GECCO’02: Proceedings of the Genetic and Evolutionary Computation Conference (Morgan Kaufmann, San Mateo, 2002), pp. 11–18

    Google Scholar 

  19. R. Battiti, G. Tecchiolli, The reactive tabu search. INFORMS J. Comput. 6(2), 126–140 (1994)

    Article  MATH  Google Scholar 

  20. J. Crispim, J. Brandão, Reactive tabu search and variable neighbourhood descent applied to the vehicle routing problem with backhauls, in Proceedings of the 4th Metaheuristics International Conference, Porto, MIC 2001 (2001), pp. 631–636

    Google Scholar 

  21. W. Crowston, F. Glover, G. Thompson, J. Trawick, Probabilistic and parametric learning combinations of local job shop scheduling rules. Technical Report, ONR research memorandum no. 117, GSIA, Carnegie-Mellon University, Pittsburgh (1963)

    Google Scholar 

  22. P. Cowling, G. Kendall, E. Soubeiga, Hyperheuristics: a tool for rapid prototyping in scheduling and optimisation, in Applications of Evolutionary Computing, EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN. Lecture Notes in Computer Science, vol. 2279 (Springer, Berlin, 2002), pp. 1–10

    Google Scholar 

  23. P. Cowling, E. Soubeiga, Neighborhood structures for personnel scheduling: a summit meeting scheduling problem (abstract), in Proceedings of the 3rd International Conference on the Practice and Theory of Automated Timetabling, Constance, Germany, ed. by E.K. Burke, W. Erben (2000)

    Google Scholar 

  24. R. Dechter, Constraint Processing (Elsevier/Morgan Kaufmann, Amsterdam/San Mateo, 2003)

    Google Scholar 

  25. S. Epstein, E. Freuder, R. Wallace, A. Morozov, B. Samuels, The adaptive constraint engine, in Principles and Practice of Constraint Programming—CP 2002, 8th International Conference. Lecture Notes in Computer Science, vol. 2470 (Springer, Berlin, 2002), pp. 525–542

    Chapter  Google Scholar 

  26. S. Epstein, E. Freuder, R. Wallace, Learning to support constraint programmers. Comput. Intell. 21(4), 336–371 (2005)

    Article  MathSciNet  Google Scholar 

  27. A.E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)

    Article  Google Scholar 

  28. N. Eén, A. Mishchenko, N. Sörensson, Applying logic synthesis for speeding up SAT, in Theory and Applications of Satisfiability Testing—SAT 2007. Lecture Notes in Computer Science, vol. 4501 (Springer, Berlin, 2007), pp. 272–286

    Chapter  Google Scholar 

  29. N. Eén, N. Sörensson, An extensible SAT-solver, in 6th International Conference on Theory and Applications of Satisfiability Testing (SAT’03), Santa Margherita Ligure, Italy, ed. by E. Giunchiglia, A. Tacchella. Lecture Notes in Computer Science, vol. 2919 (Springer, Berlin, 2003), pp. 502–518

    Chapter  Google Scholar 

  30. A. Eiben, J.E. Smith, Introduction to Evolutionary Computing. Natural Computing Series (Springer, Berlin, 2003)

    Book  MATH  Google Scholar 

  31. T. Frühwirth, S. Abdennadher, Essentials of Constraint Programming (Springer, Berlin, 2003)

    Book  Google Scholar 

  32. A. Fialho, L. Da Costa, M. Schoenauer, M. Sebag, Extreme value based adaptive operator selection, in Parallel Problem Solving from Nature—PPSN X, 10th International Conference, ed. by G. Rudolph et al. Lecture Notes in Computer Science, vol. 5199 (Springer, Berlin, 2008), pp. 175–184

    Chapter  Google Scholar 

  33. H. Fisher, L. Thompson, Probabilistic learning combinations of local job-shop scheduling rules, in Industrial Scheduling, (Prentice Hall, New York, 1963)

    Google Scholar 

  34. A. Fukunaga, Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008)

    Article  Google Scholar 

  35. C. Gebruers, A. Guerri, B. Hnich, M. Milano, Making choices using structure at the instance level within a case based reasoning framework, in Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, First International Conference, CPAIOR. Lecture Notes in Computer Science, vol. 3011 (Springer, Berlin, 2004), pp. 380–386

    Chapter  Google Scholar 

  36. F. Goualard, C. Jermann, A reinforcement learning approach to interval constraint propagation. Constraints 13(1–2), 206–226 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  37. F. Glover, G. Kochenberger, Handbook of Metaheuristics. International Series in Operations Research & Management Science (Springer, Berlin, 2003)

    MATH  Google Scholar 

  38. F. Glover, M. Laguna, Tabu Search (Kluwer Academic, Dordrecht, 1997)

    Book  MATH  Google Scholar 

  39. A. Guerri, M. Milano, Learning techniques for automatic algorithm portfolio selection, in Proceedings of the 16th European Conference on Artificial Intelligence, ECAI’2004 (IOS Press, Amsterdam, 2004), pp. 475–479

    Google Scholar 

  40. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Reading, 1989)

    MATH  Google Scholar 

  41. M. Gagliolo, J. Schmidhuber, Algorithm selection as a bandit problem with unbounded losses. Technical Report, IDSIA-07-08 (2008)

    Google Scholar 

  42. C. Gomes, B. Selman, N. Crato, H. Kautz, Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. J. Autom. Reason. 24(1/2), 67–100 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  43. Y. Hamadi, Disolver: a distributed constraint solver. Technical Report MSR-TR-2003-91, Microsoft Research (2003)

    Google Scholar 

  44. N. Hansen, Adaptive encoding: how to render search coordinate system invariant, in Parallel Problem Solving from Nature—PPSN X, 10th International Conference. Lecture Notes in Computer Science, vol. 5199 (Springer, Berlin, 2008), pp. 204–214

    Google Scholar 

  45. P.V. Hentenryck, Constraint Satisfaction in Logic Programming (MIT Press, Cambridge, 1989)

    Google Scholar 

  46. F. Hutter, Y. Hamadi, Parameter adjustment based on performance prediction: towards an instance-aware problem solver. Technical Report MSR-TR-2005-125, Microsoft Research, Cambridge, UK (2005)

    Google Scholar 

  47. F. Hutter, Y. Hamadi, H. Hoos, K. Leyton-Brown, Performance prediction and automated tuning of randomized and parametric algorithms, in CP, ed. by F. Benhamou. Lecture Notes in Computer Science, vol. 4204 (Springer, Berlin, 2006), pp. 213–228

    Google Scholar 

  48. F. Hutter, H. Hoos, K. Leyton-Brown, T. Stützle, ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)

    MATH  Google Scholar 

  49. Y. Hamadi, S. Jabbour, L. Sais, Control-based clause sharing in parallel SAT solving, in IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, ed. by C. Boutilier (2009), pp. 499–504

    Google Scholar 

  50. P.V. Hentenryck, L. Michel, Constraint-Based Local Search (MIT Press, Cambridge, 2005)

    Google Scholar 

  51. Y. Hamadi, E. Monfroy, F. Saubion, Special Issue on Autonomous Search. Constr. Program. Lett. 4 (2008)

    Google Scholar 

  52. Y. Hamadi, E. Monfroy, F. Saubion, What is autonomous search? Technical Report MSR-TR-2008-80, Microsoft Research (2008)

    Google Scholar 

  53. J. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  54. H. Hoos, SAT-encodings, search space structure, and local search performance, in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99 (Morgan Kaufmann, San Mateo, 1999), pp. 296–303

    Google Scholar 

  55. H. Hoos, An adaptive noise mechanism for WalkSAT, in AAAI/IAAI (2002), pp. 655–660

    Google Scholar 

  56. B. Hu, G. Raidl, Variable neighborhood descent with self-adaptive neighborhood-ordering, in Proc. of the 7th EU Meeting on Adaptive, Self-Adaptive and Multilevel Metaheuristics (2006)

    Google Scholar 

  57. Y. Hamadi, F. Saubion, E. Monfroy (eds.), Autonomous Search (Springer, Berlin, 2012)

    Google Scholar 

  58. F. Hutter, Automating the configuration of algorithms for solving hard computational problems. PhD thesis, Department of Computer Science, University of British Columbia (2009)

    Google Scholar 

  59. L. Ingber, Very fast simulated re-annealing. Math. Comput. Model. 12(8), 967–973 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  60. C. Janikow, Z. Michalewicz, An experimental comparison of binary and floating point representations in genetic algorithms, in Fourth International Conference on Genetic Algorithms (1991), pp. 31–36

    Google Scholar 

  61. K.D. Jong, Evolutionary Computation: A Unified Approach (MIT Press, Cambridge, 2006)

    Google Scholar 

  62. G. Kjellstroem, On the efficiency of Gaussian adaptation. J. Optim. Theory Appl. 71(3) (1991)

    Google Scholar 

  63. S. Kazarlis, V. Petridis, Varying fitness functions in genetic algorithms: studying the rate of increase of the dynamic penalty terms, in Parallel Problem Solving from Nature—PPSN V, 5th International Conference. Lecture Notes in Computer Science, vol. 1498 (1998), pp. 211–220

    Chapter  Google Scholar 

  64. O. Kramer, Self-adaptive Heuristics for Evolutionary Computation (Springer, Berlin, 2008)

    MATH  Google Scholar 

  65. F. Lobo, C. Lima, Z. Michalewicz (eds.), Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54 (Springer, Berlin, 2007)

    MATH  Google Scholar 

  66. J. Maturana, A. Fialho, F. Saubion, M. Schoenauer, M. Sebag, Extreme compass and dynamic multi-armed bandits for adaptive operator selection, in IEEE Congress on Evolutionary Computation (IEEE Press, New York, 2009), pp. 365–372

    Google Scholar 

  67. N. Mladenovic, P. Hansen, Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  68. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence (Springer, Berlin, 1992)

    Book  MATH  Google Scholar 

  69. P. Morris, The breakout method for escaping from local minima, in Proceedings of the 11th National Conference on Artificial Intelligence (AAAI) (AAAI Press, Menlo Park, 1993), pp. 40–45

    Google Scholar 

  70. K. Marriott, P. Stuckey, Programming with Constraints: An Introduction (MIT Press, Cambridge, 1998)

    MATH  Google Scholar 

  71. J. Maturana, F. Saubion, A compass to guide genetic algorithms, in Parallel Problem Solving from Nature—PPSN X, 10th International Conference, ed. by G. Rudolph et al. Lecture Notes in Computer Science, vol. 5199 (Springer, Berlin, 2008), pp. 256–265

    Chapter  Google Scholar 

  72. B. Mazure, L. Sais, E. Grégoire, Tabu search for SAT, in AAAI/IAAI (1997), pp. 281–285

    Google Scholar 

  73. B. Mazure, L. Sais, E. Grégoire, Boosting complete techniques thanks to local search methods. Ann. Math. Artif. Intell. 22(3–4), 319–331 (1998)

    Article  MATH  Google Scholar 

  74. E. Monfroy, F. Saubion, T. Lambert, On hybridization of local search and constraint propagation, in Logic Programming, 20th International Conference, ICLP 2004. Lecture Notes in Computer Science, vol. 3132 (Springer, Berlin, 2004), pp. 299–313

    Google Scholar 

  75. V. Nannen, A.E. Eiben, A method for parameter calibration and relevance estimation in evolutionary algorithms, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006 (ACM, New York, 2006), pp. 183–190

    Google Scholar 

  76. V. Nannen, A.E. Eiben, Relevance estimation and value calibration of evolutionary algorithm parameters, in IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence (2007), pp. 975–980

    Google Scholar 

  77. V. Nannen, S. Smit, A. Eiben, Costs and benefits of tuning parameters of evolutionary algorithms, in Parallel Problem Solving from Nature—PPSN X, 10th International Conference. Lecture Notes in Computer Science, vol. 5199 (Springer, Berlin, 2008), pp. 528–538

    Chapter  Google Scholar 

  78. W.J. Pullan, H. Hoos, Dynamic local search for the maximum clique problem. J. Artif. Intell. Res. 25, 159–185 (2006)

    MATH  Google Scholar 

  79. D. Patterson, H. Kautz, Auto-Walksat: a self-tuning implementation of Walksat. Electron. Notes Discrete Math. 9, 360–368 (2001)

    Article  Google Scholar 

  80. J. Puchinger, G. Raidl, Bringing order into the neighborhoods: relaxation guided variable neighborhood search. J. Heuristics 14(5), 457–472 (2008)

    Article  MATH  Google Scholar 

  81. G. Ringwelski, Y. Hamadi, Boosting distributed constraint satisfaction, in CP, ed. by P. van Beek. Lecture Notes in Computer Science, vol. 3709 (Springer, Berlin, 2005), pp. 549–562

    Google Scholar 

  82. J.R. Rice, The algorithm selection problem. Technical Report CSD-TR 152, Computer Science Department, Purdue University (1975)

    Google Scholar 

  83. S.K. Smit, A.E. Eiben, Comparing parameter tuning methods for evolutionary algorithms, in IEEE Congress on Evolutionary Computation (IEEE Press, New York, 2009), pp. 399–406

    Google Scholar 

  84. B. Selman, H. Kautz, B. Cohen, Noise strategies for improving local search, in AAAI (1994), pp. 337–343

    Google Scholar 

  85. K. Smith-Miles, Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 1–25 (2008)

    Article  Google Scholar 

  86. G. Sywerda, Uniform crossover in genetic algorithms, in Proceedings of the Third International Conference on Genetic Algorithms, San Francisco, CA, USA (Morgan Kaufmann, San Mateo, 1989), pp. 2–9

    Google Scholar 

  87. D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, in Proc. GECCO’05, ed. by H.-G. Beyer (ACM, New York, 2005), pp. 1539–1546

    Google Scholar 

  88. D. Thierens, Adaptive strategies for operator allocation, in Parameter Setting in Evolutionary Algorithms, ed. by F.G. Lobo, C.F. Lima, Z. Michalewicz (Springer, Berlin, 2007), pp. 77–90

    Chapter  Google Scholar 

  89. J. Thornton, Constraint weighting for constraint satisfaction. PhD thesis, School of Computing and Information Technology, Griffith University, Brisbane, Australia (2000)

    Google Scholar 

  90. E. Tsang, Foundations of Constraint Satisfaction, 1st edn. (Academic Press, San Diego, 1993)

    Google Scholar 

  91. T. Walsh, SAT v CSP, in Proc. of CP 2000. Lecture Notes in Computer Science, vol. 1894 (Springer, Berlin, 2000), pp. 441–456

    Google Scholar 

  92. Y.Y. Wong, K.H. Lee, K.S. Leung, C.W. Ho, A novel approach in parameter adaptation and diversity maintenance for GAs. Soft Comput. 7(8), 506–515 (2003)

    Article  Google Scholar 

  93. J. Whitacre, Q.T. Pham, R. Sarker, Credit assignment in adaptive evolutionary algorithms, in Genetic and Evolutionary Computation Conference, GECCO 2006 (ACM, New York, 2006), pp. 1353–1360

    Google Scholar 

  94. J. Whitacre, T. Pham, R. Sarker, Use of statistical outlier detection method in adaptive evolutionary algorithms, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2006), pp. 1345–1352

    Google Scholar 

  95. L. Xu, F. Hutter, H. Hoos, K. Leyton-Brown, SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32, 565–606 (2008)

    MATH  Google Scholar 

  96. B. Yuan, M. Gallagher, Statistical racing techniques for improved empirical evaluation of evolutionary algorithms, in Parallel Problem Solving from Nature—PPSN VIII, 8th International Conference, ed. by X. Yao et al. Lecture Notes in Computer Science, vol. 3242 (Springer, Berlin, 2004), pp. 172–181

    Chapter  Google Scholar 

  97. F.Y.-H. Yeh, M. Gallagher, An empirical study of Hoeffding racing for model selection in k-nearest neighbor classification, in IDEAL, ed. by M. Gallagher, J. Hogan, F. Maire. Lecture Notes in Computer Science, vol. 3578 (Springer, Berlin, 2005), pp. 220–227

    Google Scholar 

  98. B. Yuan, M. Gallagher, Combining meta-EAs and racing for difficult EA parameter tuning tasks, in Parameter Setting in Evolutionary Algorithms, ed. by F. Lobo, C. Lima, Z. Michalewicz. Studies in Computational Intelligence, vol. 54 (Springer, Berlin, 2007), pp. 121–142

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hamadi, Y. (2013). Autonomous Search. In: Combinatorial Search: From Algorithms to Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41482-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41482-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41481-7

  • Online ISBN: 978-3-642-41482-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics