Adaptive range parameter control

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Aleti, Aldeida ^a   Moser, Irene ^a   Mostaghim, Sanaz ^b  

^a SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

^b KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE PARAMETERS; OPTIMISATIONS; PARAMETER CONTROL; PARAMETER CONTROL METHOD; PARAMETER VALUES; PARAMETER-TUNING; STATIC VALUES;

OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 84866853461     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256567     Document Type: Conference Paper

References (32)

1. A. Aleti and I. Moser. Predictive parameter control. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pages 561-568, New York, NY, USA, 2011. ACM.
2. T. Bäck. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In Parallel Problem Solving from Nature 2,PPSN-II, pages 87-96. Elsevier, 1992.
3. T. Baeck, M. Schuetz, and S. Khuri. A comparative study of a penalty function, a repair heuristic and stochastic operators with the set-covering problem. Lecture Notes in Computer Science, 1063:320-332, 1996.
4. T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss. Sequential parameter optimization. In IEEE Congress on Evolutionary Computation, pages 773-780. IEEE, 2005.
5. M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp. A racing algorithm for configuring metaheuristics. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 11-18. Morgan Kaufmann Publishers, 2002.
6. K. Deb and S. Agrawal. Understanding interactions among genetic algorithm parameters. In in Foundations of Genetic Algorithms 5, pages 265-286. Morgan Kaufmann, 1999.
7. K. Deb and H.-G. Beyer. Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation, 9(2):197-221, 2001.
8. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm:NSGA-II. IEEE-Evolutionary Computation, 6:182-197, 2002.
9. A. E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transations on Evolutionary Computation, 3(2):124-141, 2007.
10. G. Eiben and M. C. Schut. New ways to calibrate evolutionary algorithms. In P. Siarry and Z. Michalewicz, editors, Advances in Metaheuristics for Hard Optimization, Natural Computing Series, pages 153-177. Springer, 2008.
11. R. Farmani and J. A. Wright. Self-adaptive fitness formulation for constrained optimization. IEEE Trans. Evolutionary Computation, 7(5):445-455, 2003.
12. A. Fialho, M. Schoenauer, and M. Sebag. Analysis of adaptive operator selection techniques on the royal road and long k-path problems. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pages 779-786. ACM, 2009.
13. D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. COMPLEX SYSTEMS, 6:333-362, 1991.
14. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159-195, 2001.
15. Z.-F. Hao, R.-C. Cai, and H. Huang. An adaptive parameter control strategy for aco. In Machine Learning and Cybernetics, 2006 International Conference on, pages 203-206, aug. 2006.
16. W. E. Hart and R. K. Belew. Optimizing an arbitrary function is hard for the genetic algorithm. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 190-195. Morgan Kaufmann, 1991.
17. J. Hesser and R. Manner. Towards an optimal mutation probability for genetic algorithms. Lecture Notes in Computer Science, 496:23-32, 1991.
18. T. Jones. A description of holland's royal road function. Evolutionary Computation, 2(4):409-415, 1994.
19. S. Malek. A User-Centric Approach for Improving a Distributed Software System's Deployment Architecture. PhD thesis, Faculty of the graduate schools, University of Southern California, 2007.
20. N. Medvidovic and S. Malek. Software deployment architecture and quality-of-service in pervasive environments. In Workshop on the Engineering of Software Services for Pervasive Environements, ESSPE, pages 47-51. ACM, 2007.
21. E. Mezura-Montes and A. Palomeque-Ortiz. Self-adaptive and deterministic parameter control in differential evolution for constrained optimization. In E. Mezura-Montes, editor, Constraint-Handling in Evolutionary Optimization, volume 198 of Studies in Computational Intelligence, pages 95-120. Springer Berlin / Heidelberg, 2009.
22. Z. Michalewicz and B. F. Fogel. How to solve it: modern heuristics. Springer-Verlag, 2004.
23. S. Mostaghim, F. Pfeiffer, and H. Schmeck. Self-organized invasive parallel optimization. In Proceedings of the International Workshop on Bio-inspired Approaches for Distributed Computing, pages 49-56, 2011.
24. V. Nannen and A. E. Eiben. Relevance estimation and value calibration of evolutionary algorithm parameters. In M. M. Veloso, editor, IJCAI'07, Proceedings of the 20th International loint Conference on Artificial Intelligence, pages 975-980, 2007.
25. Y. Papadopoulos and C. Grante. Evolving car designs using model-based automated safety analysis and optimisation techniques. The Journal of Systems and Software, 76(1):77-89, 2005.
26. A. N. Pettitt and M. A. Stephens. The kolmogorov-smirnov goodness-of-fit statistic with discrete and grouped data. Technometrics, 19(2):205-210, 1977.
27. J. Smith and T. C. Fogarty. Self adaptation of mutation rates in a steady state genetic algorithm. In International Conference on Evolutionary Computation, pages 318-323, 1996.
28. C. R. Stephens, I. G. Olmedo, J. M. Vargas, and H. Waelbroeck. Self-adaptation in evolving systems. Artificial Life, 4(2):183-201, 1998.
29. D. Thierens. An adaptive pursuit strategy for allocating operator probabilities. In H.-G. Beyer and U.-M. O'Reilly, editors, Genetic and Evolutionary Computation Conference, GECCO 2005, pages 1539-1546. ACM, 2005.
30. A. S. Wu, R. K. Lindsay, and R. L. Riolo. Empirical observations on the roles of crossover and mutation. In In Back [Bac97, pages 362-369. Morgan Kaufmann, 1997.
31. B. Yuan and M. Gallagher. Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. In Parallel Problem Solving from Nature - PPSN VIII, volume 3242 of LNCS, pages 172-181. Springer-Verlag, 2004.
32. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and da V. G. Fonseca. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Tran. on Evolutionary Comp., 7:117-132, 2003.