Rigorous analyses of simple diversity mechanisms

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 1219-1225

  Friedrich, Tobias ^a   Hebbinghaus, Nils ^a   Neumann, Frank ^a  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

Diversity; Runtime analysis; Selection procedures

Indexed keywords

COMPUTATIONAL EFFICIENCY; FUNCTION EVALUATION; POLYNOMIAL APPROXIMATION; PROBLEM SOLVING; TABU SEARCH;

DIVERSITY STRATEGY; RUNTIME ANALYSIS; SELECTION PROCEDURES;

EVOLUTIONARY ALGORITHMS;

EID: 34548089949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277194     Document Type: Conference Paper

References (17)

1. N. Chaiyaratana, T. Piroonratana, and N. Sangkawelert. Effects of diversity control in single-objective and multi-objective genetic algorithms. Journal of Heuristics, 13 (1):1-34, 2007.
2. B. Doerr, N. Hebbinghaus, and F. Neumann. Speeding up evolutionary algorithms through restricted mutation operators. In Proc. of PPSN '06, volume 4193 of LNCS, pages 978-987, 2006.
3. S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1+1) evolutionary algorithm. Theor. Comput. Sci., 276:51-81, 2002.
4. O. Giel and I. Wegener. Evolutionary algorithms and the maximum matching problem. In Proc. of STACS '03, volume 2607 of LNCS, pages 415-426, 2003.
5. J. Jägersküpper and T. Storch. How comma selection helps with the escape from local optima. In Proc. of PPSN '06, volume 4193 of LNCS, pages 52-61, 2006.
6. J. Jägersküpper and T. Storch. When the plus strategy outperforms the comma strategy - and when not. In Proc. of FOCI '01, 2007.
7. T. Jansen and I. Wegener. Evolutionary algorithms - how to cope with plateaus of constant fitness and when to reject strings of the same fitness. IEEE Trans. Evolutionary Computation, 5(6):589-599, 2001.
8. T. Jansen and I. Wegener. The analysis of evolutionary algorithms - a proof that crossover really can help. Algorithmica, 34(1):47-66, 2002.
9. H. Mühlenbein. How genetic algorithms really work: mutation and hillclimbing. In Proc. of PPSN '92, pages 15-26. Elsevier, 1992.
10. F. Neumann. Expected runtimes of evolutionary algorithms for the eulerian cycle problem. In Proc. of CEC '04, volume 1 of IEEE Press, pages 904-910, 2004.
11. F. Neumann and I. Wegener. Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. In Proc. of GECCO '04, volume 3102 of LNCS, pages 713-724, 2004.
12. G. Rudolph. How mutation and selection solve long path problems in polynomial expected time. Evolutionary Computation, 4(2): 195-205, 1996.
13. T. Storch. On the choice of the population size. In Proc. of GECCO '04, volume 3102 of LNCS, pages 748-760, 2004.
14. D. Sudholt. Crossover is provably essential for the ising model on trees. In Proc. of GECCO '05, pages 1161-1167. ACM Press, 2005.
15. R. K. Ursem. Diversity-guided evolutionary algorithms. In Proc. of PPSN '02, volume 2439 of LNCS, pages 462-474, 2002.
16. C. Witt. An analysis of the (μ+1) ea on simple pseudoboolean functions. In Proc. of GECCO '04, volume 3102 of LNCS, pages 761-773, 2004.
17. C. Witt. Worst-case and average-case approximations by simple randomized search heuristics. In Proc. of STACS '05, volume 3404 of LNCS, pages 44-56, 2005.