Attributes of dynamic combinatorial optimisation

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

Volumn 5361 LNAI, Issue , 2008, Pages 442-451

  Rohlfshagen, Philipp ^a   Yao, Xin ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMBINATORIAL OPTIMIZATION; DYNAMICS; EVOLUTIONARY ALGORITHMS;

COMBINATORIAL OPTIMISATION; CONTINUOUS DOMAINS; DO-MAINS; DYNAMIC DOMAINS; EVOLUTIONARY COMPUTATIONS; NEW ALGORITHMS; NEW APPROACHES; OPTIMISATION; QUALITY SOLUTIONS; SEARCH SPACES; SUBSET SUM PROBLEMS; UNDERLYING DYNAMICS;

COMBINATORIAL MATHEMATICS;

EID: 58349086608     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89694-4_45     Document Type: Conference Paper

References (22)

1. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2002)
2. Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Berlin (2004)
3. Weicker, K.: Evolutionary algorithms and dynamic optimization problems. Der Andere Verlag (2003)
4. Yang, S., Ong, Y.S., Jin, Y. (eds.): Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Heidelberg (2007)
5. Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithms. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.C., McKay, R., Essam, D., Gedeon, T. (eds.) Proceedings of the 2003 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2246-2253 (2003)
6. Ursem, R.K., Krink, T., Jensen, M.T., Michalewicz, Z.: Analysis and modeling of control tasks in dynamic systems. IEEE Transactions on Evolutionary Computation 6(4), 378-389 (2002)
7. Branke, J., Salihoglu, E., Uyar, S.: Towards an analysis of dynamic environments. In: Beyer, H.G., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 1433-1439. ACM, New York (2005)
8. Branke, J., Orbayi, M., Uyar, S.: The role of representations in dynamic knapsack problem. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 764-775. Springer, Heidelberg (2006)
9. Bosman, P.A.N., Poutre, H.L.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference, pp. 1165-1172 (2007)
10. Rossi, C., Barrientes, A., del Cerro, J.: Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategies. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 697-704 (2007)
11. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environment - a survey. IEEE Transactions on Evolutionary Computation 9(3), 303-317 (2005)
12. Branke, J.: Evolutionary approaches to dynamic environments - updated survey. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 27-30 (2001)
13. Morrison, R.W., DeJong, K.A.: A test problem generator for non-stationary environments. In: Congress on Evolutionary Computation, vol. 3, pp. 2047-2053. IEEE, Los Alamitos (1999)
14. Merz, P.: Advanced fitness landscape analysis and the performance of memetic algorithms. Evolutionary Computation 12(3), 303-325 (2004)
15. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)
16. Wolpert, D.H., MacReady, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82 (1997)
17. Rothlauf, F.: Representations for genetic and evolutionary algorithms. Springer, Heidelberg (2002)
18. Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Grefenstette, J.J. (ed.) Second International Conference on Genetic Algorithms, pp. 59-68. Lawrence Erlbaum Associates, Mahwah (1987)
19. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 IEEE Congress on Evolutionary Computation, vol. 3, pp. 1875-1882. IEEE, Los Alamitos (1999)
20. Yamasaki, K., Sekiguchi, K.K.M.: Dynamic optimization by evolutionary algorithms applied to financial time series. In: Proceedings of the 2002 Congress on Evolutionary Computation (2002)
21. 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 (1995)
22. Wong, R.L.: A genetic algorithm for subset sum problem. Neurocomputing 57, 463-468 (2004)