A large population size can be unhelpful in evolutionary algorithms

Theoretical Computer Science

Volumn 436, Issue , 2012, Pages 54-70

  Chen, Tianshi ^a,b   Tang, Ke ^b   Chen, Guoliang ^b   Yao, Xin ^b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Combinatorial optimization; Computational time complexity; Evolutionary algorithms; Evolutionary computation; Runtime analysis

Indexed keywords

ANALYTICAL APPROACH; COMPUTATIONAL EXPERIMENT; COMPUTATIONAL TIME COMPLEXITY; EVOLUTIONARY ALGORITHMS (EAS); LARGE POPULATION; MULTIMODAL PROBLEMS; POLYNOMIAL-TIME; POPULATION DIVERSITY; POPULATION SIZES; PROBLEM INSTANCES; RUN-TIME ANALYSIS; RUNTIMES;

COMBINATORIAL OPTIMIZATION; POLYNOMIAL APPROXIMATION; POPULATION STATISTICS;

EVOLUTIONARY ALGORITHMS;

EID: 84860773062     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2011.02.016     Document Type: Article

References (17)

1. T. Chen, J. He, G. Chen, and X. Yao Choosing selection pressure for wide-gap problems Theoretical Computer Science 411 6 2010 926 934
2. T. Chen, J. He, G. Sun, G. Chen, and X. Yao A new approach to analyzing average time complexity of population-based evolutionary algorithms on unimodal problems IEEE Transactions on System, Man, and Cybernetics, Part B 39 5 2009 1092 1106
3. T. Chen, K. Tang, G. Chen, and X. Yao Analysis of computational time of simple estimation of distribution algorithms IEEE Transactions on Evolutionary Computation 14 1 2010 1 22
4. S. Droste, T. Jansen, and I. Wegener On the analysis of the (1 + 1) evolutionary algorithm Theoretical Computer Science 276 1-2 2002 51 81
5. T. Friedrich, P.S. Oliveto, D. Sudholt, C. Witt, Theoretical analysis of diversity mechanisms for global exploration, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'08), Atlanta, Georgia, USA, 2008, pp. 945-952.
6. D.E. Goldberg, and K. Deb A comparative analysis of selection scheme used in genetic algorithms Foundations of Genetic Algorithms 1991 Morgan Kaufmann Publishers 69 93
7. J. He, and X. Yao Drift analysis and average time complexity of evolutionary algorithms Artificial Intelligence 127 1 March 2001 57 85
8. J. He, and X. Yao From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms IEEE Transactions on Evolutionary Computation 6 5 2002 495 511
9. J. He, and X. Yao A study of drift analysis for estimating computation time of evolutionary algorithms Natural Computing 3 1 2004 21 35
10. T. Jansen, K.A.D. Jong, and I. Wegener On the choice of the offspring population size in evolutionary algorithms Evolutionary Computation 13 4 2005 413 440
11. P.K. Lehre, X. Yao, On the impact of mutation-selection balance on the runtime of evolutionary algorithms, in: IEEE Transactions on Evolutionary Computation, conditionally accepted on 12 November 2010.
12. R. Motwani, and P. Raghavan Randomized Algorithms 1995 Cambridge University Press
13. P.S. Oliveto, J. He, X. Yao, Evolutionary algorithms and the vertex cover problem, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'07), Singapore, September 25-28, 2007, pp. 1870-1877.
14. P.S. Oliveto, J. He, and X. Yao Analysis of the (1+1)-EA for finding approximate solutions to vertex cover problems IEEE Transactions on Evolutionary Computation 13 5 2009 1006 1029
15. A. Papoulis Probability, Random Variables, and Stochastic Processes 1991 McGraw-Hill
16. M.R. Spiegel Theory and Problems of Probability and Statistics 1992 McGraw-Hill New York
17. C. Witt, An analysis of the (μ + 1) EA on simple pseudo-boolean functions, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'04), Seattle, Washington, USA, 2004, pp. 761-773.