Abstract
An Autonomous Search system should have the ability to advantageously modify its internal components when exposed to changing external forces and opportunities. It corresponds to a particular case of adaptive systems, with the objective of improving its problem-solving performance by adapting its search strategy to the problem at hand. Internal components correspond to the various algorithms involved in the search process: heuristics, inference mechanisms, etc. External forces correspond to the evolving information collected during this search process: search landscape analysis (quality, diversity, entropy, etc), external knowledge (prediction models, rules, etc) and so on.
In 2007, we organized the first workshop on Autonomous Search in Providence, RI, USA, in order to present relevant works aimed at building more intelligent solvers for the constraint programming community. We tried to describe more conceptually the concept of Autonomous Search from the previously described related works. The purpose of this book is to provide a clear overview of recent advances in autonomous tools for optimization and constraint satisfaction problems. In order to be as exhaustive as possible, keeping the focus on constrained problem solving, this book includes ten chapters that cover different solving techniques from metaheuristics to tree-based search and that illustrate how these solving techniques may benefit from intelligent tools by improving their efficiency and adaptability to solve the problems at hand.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bader-El-Den, M., Poli, R.: Generating SAT local-search heuristics using a GP hyper-heuristic Framework. In: 8th International Conference, Evolution Artificielle, EA 2007. Revised Selected Papers, Springer, no. 4926 in Lecture Notes in Computer Science, pp, 37–49 (2008)
Battiti, R., Brunato, M. (eds), Learning and Intelligent Optimization second international conference, LION 2007 II. Selected Papers, Lecture Notes in Computer Science, vol., 5313. Springer (2008)
Battiti, R., Brunato, M.: Handbook of metaheuristics (2nd edition), Gendreau, M., Potvin, J. Y. (eds), Springer, chap, Reactive search optimization: learning while optimizing (2009)
Battiti, R., Tecchiolli, G.: The reactive tabu search. INFORMS Journal on Computing 6(2):126–140 (1994)
Battiti, R., Brunato, M., Mascia, F.: Reactive search and intelligent optimization, Operations research/Computer Science Interfaces, vol., 45. Springer (2008)
Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Handbook of Meta-heuristics, Kluwer, chap., Hyper-heuristics: an emerging direction in modern search technology, pp, 457–474 (2003)
Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R. A.: Survey of hyper-heuristics. Tech. Rep. Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham, Computer Science (2009)
Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: Handbook of metaheuristics (2nd edition), Gendreau, M., Potvin, J. Y. (eds), Springer, chap., A classification of hyper-heuristics approaches (2009)
Cowling, P., Soubeiga, E.: Neighborhood structures for personnel scheduling: a summit meeting scheduling problem (abstract). In: Burke, E., Erben, W. (eds), Proceedings of the 3rd International Conference on the Practice and Theory of Automated Timetabling (2000)
Cowling, P., Kendall, G., Soubeiga, E.: Hyperheuristics: a tool for rapid prototyping in scheduling and optimisation. In: Applications of Evolutionary Computing, EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN, Springer, Lecture Notes in Computer Science, vol., 2279, pp, 1–10 (2002)
Crowston, W., Glover, F., Thompson, G., Trawick, J.: Probabilistic and parametric learning combinations of local job shop scheduling rules. Tech. rep., ONR Research Memorandum No. 117, GSIA, Carnegie-Mellon University, Pittsburg, PA (1963)
De Jong, K.: Parameter setting in EAs: a 30 year perspective. In: [25], pp, 1–18 (2007)
Eiben, A. E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans Evolutionary Computation 3(2):124–141 (1999)
Eiben, A. E., Michalewicz, Z., Schoenauer, M., Smith, J. E.: Parameter control in evolutionary algorithms. In: [25], pp, 19–46 (2007)
Fisher, H., Thompson, L.: Industrial Scheduling, Prentice Hall, chap., Probabilistic learning combinations of local job-shop scheduling rules (1963)
Gagliolo, M., Schmidhuber, J.: Algorithm selection as a bandit problem with unbounded losses. Tech. rep., Tech. report IDSIA - 07 - 08 (2008)
Hamadi, Y., Monfroy, E., Saubion, F.: Special issue on Autonomous Search. Constraint Programming Letters 4, URL http://www.constraint-programming-letters.org/ (2008)
Hamadi, Y., Monfroy, E., Saubion, F.: Hybrid Optimization: The Ten Years of CPAIOR, Springer, chap., What is autonomous search? (2010)
Hoos, H.: An adaptive noise mechanism for WalkSAT. In: AAAI/IAAI, pp, 655–660 (2002)
Hutter, F.: Automating the configuration of algorithms for solving hard computational problems. Ph.D. thesis, Department of Computer Science, University of British Columbia (2009)
Hutter, F., Hamadi, Y., Hoos, H., Brown, K. L.: Performance prediction and automated tuning of randomized and parametric algorithms. In: Twelfth International Conference on Principles and Practice of Constraint Programming CP’06 (2006)
Hutter, F., Hoos, H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proc. of the Twenty-Second Conference on Artifical Intelligence (AAAI ’07), pp, 1152–1157 (2007)
Ingber, L.: Very fast simulated re-annealing. Mathematical Computer Modelling 12(8):967–973 (1989)
Laurière, J. L.: Intelligence Artificielle, Résolution de problèmes par l’Homme et la machine. Eyrolles (1986)
Lobo, F., Lima, C., Michalewicz, Z. (eds), Parameter setting in evolutionary algorithms, Studies in Computational Intelligence, vol., 54. Springer (2007)
Nannen, V., Eiben, A. E.: A method for parameter calibration and relevance estimation in evolutionary algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 2006, Proceedings, ACM, pp, 183–190 (2006)
Nannen, V., Eiben, A. E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp, 975–980 (2007)
Nannen, V., Smit, S., Eiben, A. E.: Costs and benefits of tuning parameters of evolutionary algorithms. In: Parallel Problem Solving from Nature, PPSN X, 10th International Conference, Springer, Lecture Notes in Computer Science, vol., 5199, pp, 528–538 (2008)
Patterson, D., Kautz, H.: Auto-Walksat: a self-tuning implementation of walksat. Electronic Notes in Discrete Mathematics 9:360–368 (2001)
Poli, R., Graff, M.: There is a free lunch for hyper-heuristics, genetic programming and computer scientists. In: EuroGP, Springer, Lecture Notes in Computer Science, vol., 5481, pp, 195–207 (2009)
Rice, J.: The algorithm selection problem. Tech. Rep. CSD-TR 152, Computer Science Department, Purdue University (1975)
Smit, S., Eiben, A. E.: Comparing parameter tuning methods for evolutionary algorithms. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009), IEEE, pp, 399–406 (2009)
Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41(1):1–25 (2008)
Wolpert, D. H., Macready, W. G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82 (1997)
Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: Portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32:565–606 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hamadi, Y., Monfroy, E., Saubion, F. (2011). An Introduction to Autonomous Search. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds) Autonomous Search. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21434-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-21434-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21433-2
Online ISBN: 978-3-642-21434-9
eBook Packages: Computer ScienceComputer Science (R0)