Parameter control methods for selection operators in genetic algorithms

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

Volumn 5199 LNCS, Issue , 2008, Pages 620-630

  Vajda, Péter ^a   Eiben, Agoston E ^a   Hordijk, Wiebe ^a  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY COMPUTATIONS; EXPERIMENTAL COMPARISONS; PARAMETER CONTROL METHODS; PARAMETER CONTROLS; PERFORMANCE BENEFITS; SELECTION OPERATORS; SELECTION PRESSURES; TEST FUNCTIONS; EXPERIMENTAL COMPARISON; ON THE FLIES; PARAMETER CONTROL; PARAMETER CONTROL METHOD;

DIESEL ENGINES; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; PROBLEM SOLVING; ALGORITHMS; PARAMETER ESTIMATION;

GENETIC ENGINEERING; PROBLEM SOLVING;

EID: 56449103695     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87700-4_62     Document Type: Conference Paper

References (18)

1. Bäck, T.: Evolutionary algorithms in theory and practice, Oxford University Press, Oxford (1996)
2. Bäck, T., Schütz, M.: Intelligent mutation rate control in canonical genetic algorithms. in: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 158-167. Springer, Heidelberg (1996)
3. DeJong, K.: Parameter setting in EAs: a 30 year perspective. In: Parameter Setting in Evolutionary Algorithms, pp. 1-18. Springer, Heidelberg (2007)
4. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124-141 (1999)
5. Eiben, A.E., Schut, M.C., de Wilde, A.R.: Boosting genetic algorithms with self-adaptive selection. in: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1584-1589 (2006)
6. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, Corrected reprint. Springer, Heidelberg (2007)
7. Yager, R.R., et al.: Fuzzy Sets and Applications: Selected Papers by L.A. Zadeh. John Wiley, New York (1987)
8. Herrera, F., Lozano, M.: Fuzzy genetic algorithms: issues and models. Technical report, No. 18071, Granada, Spain (1999)
9. Hohn, C., Reeves, C: Are long path problems hard for genetic algorithms? in: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 134-143. Springer, Heidelberg (1996)
10. Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press (1975)
11. Jansen, T., De Jong, K., Wegener, 1.: On the choice of offspring population size in evolutionary algorithms. Evolutionary Computation 13(4), 413-440 (2005)
12. De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial intelligence (5), 1-26 (1992)
13. Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Studies in Computational intelligence, vol. 54. Springer, Heidelberg (2007)
14. Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: a genetic algorithm. Parallel Computing 21(1), 1-28 (1995)
15. Mitchell, M., Forrest, S., Holland, J.H.: The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, Paris, 11-13, 1992, pp. 245-254. A Bradford book, The MIT Press (1992)
16. Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, Chichester (1995)
17. Spears, W.: Evolutionary algorithms: the role of mutation and recombination. Springer, Heidelberg (2000)
18. Whitley, D.: Fundamental principles of deception. In: Morgan Kaulmann (ed.) Foundations of Genetic Algorithms, pp. 221-241. Morgan Kaulmann, San Francisco (1991)