Adaptive population models for offspring populations and parallel evolutionary algorithms

FOGA'11 - Proceedings of the 2011 ACM/SIGEVO Foundations of Genetic Algorithms XI

Volumn , Issue , 2011, Pages 181-192

  Lässig, Jörg ^a   Sudholt, Dirk ^b  

^a UNIVERSITY OF LUGANO   (Switzerland)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Dynamic population size; Island model; Offspring populations; Parallel evolutionary algorithms; Run time analysis; Spatial structures

Indexed keywords

DYNAMIC POPULATION; ISLAND MODEL; OFFSPRING POPULATIONS; PARALLEL EVOLUTIONARY ALGORITHMS; RUN-TIME ANALYSIS; SPATIAL STRUCTURE;

GENETIC ALGORITHMS;

POPULATION STATISTICS;

EID: 79956051118     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1967654.1967671     Document Type: Conference Paper

References (18)

1. E. Alba. Parallel metaheuristics: A new class of algorithms, 2005.
2. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. The MIT Press, 2nd edition, 2001.
3. J. Costa, R. Tavares, and A. Rosa. Experimental study on dynamic random variation of population size. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1999.
4. S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276:51-81, 2002.
5. A. Eiben, E. Marchiori, and V. Valko. Evolutionary algorithms with on-the-fly population size adjustment. In Parallel Problem Solving from Nature (PPSN 2004), pages 41-50. Springer, 2004. (Pubitemid 39750205)
6. N. Hansen, A. Gawelczyk, and A. Ostermeier. Sizing the population with respect to the local progress in (1, λ)-evolution strategies-A theoretical analysis. In 1995 IEEE International Conference on Evolutionary Computation, pages 80-85, 1995.
7. G. Harik and F. Lobo. A parameter-less genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pages 258-265, 1999.
8. M. Herdy. The number of offspring as strategy parameter in hierarchically organized evolution strategies. ACM SIGBIO Newsletter, 13(2):9, 1993.
9. T. Jansen, K. A. De Jong, and I. Wegener. On the choice of the offspring population size in evolutionary algorithms. Evolutionary Computation, 13:413-440, 2005. (Pubitemid 43955277)
10. J. Lässig and D. Sudholt. The benefit of migration in parallel evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010), pages 1105-1112, 2010.
11. J. Lässig and D. Sudholt. General scheme for analyzing running times of parallel evolutionary algorithms. In 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010), volume 6238 of LNCS, pages 234-243. Springer, 2010.
12. Z. Michalewicz. Genetic algorithms + data structures = evolution programs. Springer, 1996.
13. G. Rudolph. How mutation and selection solve long-path problems in polynomial expected time. Evolutionary Computation, 4(2):195-205, 1997. (Pubitemid 126706156)
14. D. Schlierkamp-Voosen and H. Mühlenbein. Strategy adaptation by competing subpopulations. Parallel Problem Solving from Nature (PPSN III), pages 199-208, 1994.
15. H.-P. Schwefel. Numerical optimization of computer models. John Wiley & Sons, Inc. New York, NY, USA, 1981.
16. D. Sudholt. General lower bounds for the running time of evolutionary algorithms. In 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010), volume 6238 of LNCS, pages 124-133. Springer, 2010.
17. M. Tomassini. Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer, 2005.
18. I. Wegener. Methods for the analysis of evolutionary algorithms on pseudo-Boolean functions. In R. Sarker, X. Yao, and M. Mohammadian, editors, Evolutionary Optimization, pages 349-369. Kluwer, 2002.