Analysis of an asymmetric mutation operator

Evolutionary Computation

Volumn 18, Issue 1, 2010, Pages 1-26

  Jansen, Thomas ^a   Sudholt, Dirk ^b  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b INTERNATIONAL COMPUTER SCIENCE INSTITUTE   (United States)

Author keywords

Evolutionary algorithms; Mutation; Runtime analysis; Unimodal problems

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER SIMULATION; METHODOLOGY; SEARCH ENGINE; THEORETICAL MODEL; TIME;

ALGORITHMS; COMPUTER SIMULATION; MODELS, THEORETICAL; SEARCH ENGINE; TIME FACTORS;

EID: 77749286235     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/evco.2010.18.1.18101     Document Type: Article

References (23)

1. Brémaud, P. (1998). Markov chains: Gibbs fields, Monte Carlo simulation, and queues. Berlin: Springer.
2. Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., and Zitzler, E. (2007). Do additional objectives make a problem harder? In Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 765-772, New York: ACM.
3. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2002). Introduction to Algorithms. Cambridge, MA: MIT Press.
4. Doerr, B., Hebbinghaus, N., and Neumann, F. (2007). Speeding up evolutionary algorithms through unsymmetric mutation operators. Evolutionary Computation (Special Issue on Bridging Theory and Practice), 15:401-410.
5. Doerr, B., and Johannsen, D. (2007). Adjacency list matchings-An ideal genotype for cycle covers. In Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 1203-1210. New York: ACM.
6. Droste, S., Jansen, T., Tinnefeld, K., and Wegener, I. (2003). A new framework for the valuation of algorithms for black-box optimization. In Foundations of Genetic Algorithms 7 (FOGA), pp. 253-270. San Mateo, CA: Morgan Kaufmann.
7. Droste, S., Jansen, T., and Wegener, I. (2002). On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276:51-81.
8. Droste, S., Jansen, T., and Wegener, I. (2006). Upper and lower bounds for randomized search heuristics in black-box optimization. Theory of Computing Systems, 39(4):525-544.
9. Droste, S., and Wiesmann, D. (2003). On the design of problem-specific evolutionary algorithms. In Advances in Evolutionary Computing: Theory and Applications, pp. 153-173. Berlin: Springer-Verlag.
10. Garnier, J., Kallel, L., and Schoenauer, M. (1999). Rigorous hitting times for binary mutations. Evolutionary Computation, 7(2):173-203.
11. He, J., and Yao, X.(2004). Astudyofdriftanalysis forestimatingcomputationtimeofevolutionary algorithms. Natural Computing, 3(1):21-35.
12. Igel, C., and Toussaint, M. (2004). A no-free-lunch theorem for non-uniform distributions of target functions. Journal of Mathematical Modelling and Algorithms, 3(4):313-322.
13. Jansen, T., De Jong, K. A., and Wegener, I. (2005). On the choice of the offspring population size in evolutionary algorithms. Evolutionary Computation, 13:413-440.
14. Jansen, T., and Sudholt, D. (2005). Design and analysis of an asymmetric mutation operator. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pp. 497-504. Piscataway, NJ: IEEE Press.
15. Jansen, T., and Wegener, I. (2001). Evolutionary algorithms-How to cope with plateaus of constant fitness and when to reject strings of the same fitness. IEEE Transactions on Evolutionary Computation, 5:589-599.
16. Macready, W., and Wolpert, D. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82.
17. Motwani, R., and Raghavan, P. (1995). Randomized algorithms. Cambridge, UK: Cambridge University Press.
18. Neumann, F., and Wegener, I. (2004). Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. In Genetic and Evolutionary Computation Conference (GECCO 2004), pp. 713-724. Berlin: Springer. LNCS 3102.
19. Oliveto, P. S., He, J., and Yao, X. (2007). Computational complexity analysis of evolutionary algorithms for combinatorial optimization: A decade of results. International Journal of Automation and Computing, 4(3):281-293.
20. Quick, R. J., Rayward-Smith, V. J., and Smith, G. D. (1998). Fitness distance correlation and ridge functions. In Parallel Problem Solving from Nature (PPSN V), pp. 77-86. Berlin: Springer.
21. Raidl, G. R., Koller, G., and Julstrom, B. A. (2006). Biased mutation operators for subgraphselection problems. IEEE Transactions on Evolutionary Computation, 10(2):145-156.
22. Scheideler, C. (2000). Probabilistic methods for coordination problems. University of Paderborn. Habilitation Thesis. Retrieved December 2, 2009 from http://www.cs.unipaderborn.de/fachgebiete/fg-ti/personen/scheideler.html.
23. Schumacher, C., Vose, M., and Whitley, D. (2001). The no free lunch and problem description length. In Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 565-570. San Mateo, CA: Morgan Kaufmann.