Differential evolution with noise analyzer

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

Volumn 5484 LNCS, Issue , 2009, Pages 715-724

  Caponio, Andrea ^a   Neri, Ferrante ^b  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b POLITECNICO DI BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EVOLUTION; FITNESS LANDSCAPE; HIGH-QUALITY SOLUTIONS; META HEURISTICS; NOISE ANALYSIS; NOISE BANDWIDTH; NOISY ENVIRONMENT; NOISY OPTIMIZATION; NUMERICAL OPTIMIZATIONS; NUMERICAL RESULTS; PAIR-WISE COMPARISON; SCALE FACTOR; SEARCH DIRECTION; STOCHASTIC PROCESS;

COMPUTER SCIENCE;

EVOLUTIONARY ALGORITHMS;

EID: 67650660957     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01129-0_81     Document Type: Conference Paper

References (22)

1. Branke, J., Schmidt, C.: Selection in the presence of noise. In: Cantu-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol.2723, pp. 766-777. Springer, Heidelberg (2003) (Pubitemid 36892897)
2. Di Pietro, A., While, L., Barone, L.: Applying evolutionary algorithms to problems with noisy, time-consuming fitness functions. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol.2, pp. 1254-1261 (2004)
3. Beyer, H.G., Sendhoff, B.: Functions with noise-induced multimodality: a test for evolutionary robust optimization-properties and performance analysis. IEEE Transactions on Evolutionary Computation 10(5), 507-526 (2006) (Pubitemid 44560740)
4. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey
5. Fitzpatrick, J.M., Grefenstette, J.J.: Genetic algorithms in noisy environments. Machine Learning 3, 101-120 (1988)
6. Beyer, H.G.: Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory. Evolutionary Computation 1(2), 165-188 (1993)
7. Aizawa, A.N.,Wah, B.W.: Scheduling of genetic algorithms in a noisy environment. Evolutionary Computation 2(2), 97-122 (1994)
8. Stagge, P.: Averaging efficiently in the presence of noise. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol.1498, pp. 188-200. Springer, Heidelberg (1998) (Pubitemid 128145950)
9. Neri, F., Cascella, G.L., Salvatore, N., Kononova, A.V., Acciani, G.: Prudent-daring vs tolerant survivor selection schemes in control design of electric drives. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol.3907, pp. 805-809. Springer, Heidelberg (2006) (Pubitemid 44019266)
10. Branke, J., Schmidt, C., Schmeck, H.: Efficient fitness estimation in noisy environments. In: Spector, L., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 243-250. Morgan Kaufmann, San Francisco (2001)
11. Sano, Y., Kita, H., Kamihira, I., Yamaguchi, M.: Online optimization of an engine controller by means of a genetic algorithm using history of search. In: Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning, pp. 2929-2934. Springer, Heidelberg (2000) (Pubitemid 34089678)
12. Neri, F., del Toro Garcia, X., Cascella, G.L., Salvatore, N.: Surrogate assisted local search on PMSM drive design. COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering 27(3), 573-592 (2008) (Pubitemid 351631200)
13. Markon, O., Arnold, D.V., Biick, T., Beielstein, T., Beyer, H.G.: Thresholding-a selection operator for noisy ES. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 465-472 (2001)
14. Branke, J., Schmidt, C.: Sequential sampling in noisy environments. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol.3242, pp. 202-211. Springer, Heidelberg (2004) (Pubitemid 39750221)
15. Bartz-Beielstein, T., Blum, D., Branke, J.: Particle swarm optimization and sequential sampling in noisy environments. In: Doerner, K.F., et al. (eds.) Metaheuristics. Operations Research/Computer Science Interfaces Series, vol.39, pp. 261-273. Springer, Heidelberg (2007)
16. Pan, H., Wanga, L., Liu: Particle swarm optimization for function optimization in noisy environment. Applied Mathematics and Computation 181, 908-919 (2006) (Pubitemid 44646568)
17. Klamargias, A.D., Parsopoulos, K.E., Alevizos, P.D., Vrahatis, M.N.: Particle filtering with particle swarm optimization in systems with multiplicative noise. In: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pp. 57-62. ACM, New York (2008)
18. Krink, T., Filipić, B., Fogel, G.B.: Noisy optimization problems-a particular challenge for differential evolution? In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 332-339 (2004)
19. Das, S., Konar, A.: An improved differential evolution scheme for noisy optimization problems. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol.3776, pp. 417-421. Springer, Heidelberg (2005) (Pubitemid 43751678)
20. Das, S., Konar, A., Chakraborty, U.: Improved differential evolution algorithms for handling noisy optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol.2, pp. 1691-1698 (2005) (Pubitemid 41496053)
21. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution for optimization of noisy problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1865-1872 (2006) (Pubitemid 47130723)
22. NIST/SEMATECH: e-handbook of statistical methods, http://www.itl.nist. gov/div898/handbook/