A meta-learning prediction model of algorithm performance for continuous optimization problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7491 LNCS, Issue PART 1, 2012, Pages 226-235

  Muñoz, Mario A ^a   Kirley, Michael ^a   Halgamuge, Saman K ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

algorithm configuration; Automatic analysis of algorithms; heuristic methods; meta learning models; randomized algorithms

Indexed keywords

ACCURATE PREDICTION; ALGORITHM PARAMETERS; ALGORITHM PERFORMANCE; ALGORITHM SELECTION; AUTOMATIC ANALYSIS; BACK PROPAGATION NEURAL NETWORKS; CONTINUOUS OPTIMIZATION; CONTINUOUS OPTIMIZATION PROBLEMS; FEED-FORWARD; LANDSCAPE ANALYSIS; LEARNING FRAMEWORKS; LEARNING STRATEGY; METALEARNING; OPTIMIZATION PROBLEMS; PREDICTION MODEL; PROBLEM INSTANCES; RANDOMIZED ALGORITHMS;

HEURISTIC METHODS; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; PARAMETER ESTIMATION;

MATHEMATICAL MODELS;

EID: 84866348009     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32937-1_23     Document Type: Conference Paper

References (26)

1. Hutter, F., Hamadi, Y., Hoos, H.H., Leyton-Brown, K.: Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 213-228. Springer, Heidelberg (2006)
2. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67-82 (1997)
3. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Empirical hardness models: Methodology and a case study on combinatorial auctions. J. ACM 56, 22:1-22:52 (2009)
4. Rice, J.: The algorithm selection problem. In: Advances in Computers, vol.15, pp. 65-118. Elsevier (1976)
5. Smith-Miles, K.A., James, R.J.W., Giffin, J.W., Tu, Y.: A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance. In: Stützle, T. (ed.) LION3. LNCS, vol. 5851, pp. 89-103. Springer,Heidelberg (2009)
6. Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70-80 (2012)
7. Hansen,N.,Auger,A., Finck, S.,Ros, R.:Real-parameter black-box optimization benchmarking BBOB-2010: Experimental setup. Technical Report RR-7215, INRIA (September 2010)
8. Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, NTU, Singapore and IIT, Kanpur (2005)
9. Reeves, C.: Fitness landscapes. In: Search Methodologies, pp. 587-610. Springer (2005)
10. Hough, P., Williams, P.: Modern machine learning for automatic optimization algorithm selection. In: Proceedings of the INFORMS Artificial Intelligence and Data Mining Workshop (2006)
11. He, J., Reeves, C., Witt, C., Yao, X.: A note on problem difficulty measures in black-box optimization: Classification, realizations and predictability. Evol. Comput. 15(4), 435-443 (2007)
12. Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 6:1-6:25 (2009)
13. Francois, O., Lavergne, C.: Design of evolutionary algorithms-a statistical perspective. IEEE Trans. Evol. Comput. 5(2), 129-148 (2001)
14. Steer, K.C.B.,Wirth, A., Halgamuge, S.K.: Information Theoretic Classification of Problems for Metaheuristics. In: Li, X., Kirley,M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 319-328. Springer, Heidelberg (2008)
15. Malan, K., Engelbrecht, A.: Quantifying ruggedness of continuous landscapes using entropy. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1440-1447 (May 2009)
16. Caamaño, P., Prieto, A., Becerra, J.A., Bellas, F., Duro, R.J.: Real-ValuedMultimodal Fitness Landscape Characterization for Evolution. In:Wong, K.W.,Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 567-574. Springer, Heidelberg (2010)
17. Mersmann, O., Preuss, M., Trautmann, H.: Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 73-82. Springer, Heidelberg (2010)
18. Müller, C.L., Sbalzarini, I.F.: Global Characterization of the CEC 2005 Fitness Landscapes Using Fitness-Distance Analysis. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 294-303. Springer, Heidelberg (2011)
19. Richter, H.: Coupled map lattices as spatio-temporal fitness functions: Landscape measures and evolutionary optimization. Phys. Nonlinear Phenom. 237(2), 167-186 (2008)
20. Watson, J., Howe, A.: Focusing on the individual: Why we need new empirical methods for characterizing problem difficulty. In: Working Notes of ECAI 2000 Workshop on Empirical Methods in Artificial Intelligence (August 2000)
21. Beck, J., Watson, J.: Adaptive search algorithms and fitness-distance correlation. In: Proceedings of the Fifth Metaheuristics International Conference (2003)
22. Smith, T., Husbands, P., Layzell, P., O'Shea, M.: Fitness landscapes and evolvability. Evol. Comput. 10(1), 1-34 (2002)
23. Lunacek,M.,Whitley, D.: The dispersion metric and theCMAevolution strategy. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 477-484. ACM, New York (2006)
24. Jones, T., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 184-192. Morgan Kaufmann Publishers Inc. (1995)
25. Seo, D., Moon, B.: An information-theoretic analysis on the interactions of variables in combinatorial optimization problems. Evol. Comput. 15(2), 169-198 (2007)
26. Rice, J.: Methodology for the algorithm selection problem. In: Proceedings of the IFIP TC 2.5 Working Conference on Performance Evaluation of Numerical Software (1979)