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Crossover can provably be useful in evolutionary computation
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 10th annual conference on Genetic and evolutionary computation table of contents
Atlanta, GA, USA
SESSION: Evolutionary combinatorial optimization papers table of contents
Pages 539-546  
Year of Publication: 2008
ISBN:978-1-60558-130-9
Authors
Benjamin Doerr  Max-Planck-Institut für Informatik, Saarbrücken, Germany
Edda Happ  Max-Planck-Institut für Informatik, Saarbrücken, Germany
Christian Klein  Max-Planck-Institut für Informatik, Saarbrücken, Germany
Sponsors
ACM: Association for Computing Machinery
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
Publisher
ACM  New York, NY, USA
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ABSTRACT

We show that the natural evolutionary algorithm for the all-pairs shortest path problem is significantly faster with a crossover operator than without. This is the first theoretical analysis proving the usefulness of crossover for a non-artificial problem.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
C. W. Ahn and R. Ramakrishna. A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans. Evolutionary Computation, 6:566--579, 2002.
 
2
N. Alon and J. H. Spencer. The Probabilistic Method. Wiley, 2nd edition, 2000.
 
3
 
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B. Doerr, E. Happ, and C. Klein. A tight bound for the (11)-EA on the single source shortest path problem. In Proc. of CEC '07, pages 1890--1895. IEEE Press, 2007.
 
5
S. Fischer and I. Wegener. The Ising model on the ring: Mutation versus recombination. In Proc. of GECCO '04, volume 3102 of LNCS, pages 1113--1124. Springer, 2004.
6
 
7
 
8
 
9
 
10
J. Inagaki, M. Haseyama, and H. Kitajima. A genetic algorithm for determining multiple routes and its applications. In Proc. of ISCAS '99, pages 137--140. IEEE Press, 1999.
 
11
 
12
 
13
14
 
15
S. Kirkpatrick, D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing. Science, 220:671--680, 1983.
 
16
 
17
S. Liang, A. N. Zincir-Heywood, and M. I. Heywood. Adding more intelligence to the network routing problem: AntNet and Ga-agents. Appl. Soft Comput., 6:244--257, 2006.
 
18
 
19
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equations of state calculations by fast computing machines. J. Chemical Physics, 21:1087--1091, 1953.
 
20
21
 
22
Y. Rabinovich and A. Wigderson. An analysis of a simple genetic algorithm. In Proc. of ICGA '94, pages 215--221, 1991.
 
23
 
24
J. Scharnow, K. Tinnefeld, and I. Wegener. The analysis of evolutionary algorithms on sorting and shortest paths problems. J. Math. Model. Algorithms, 3:349--366, 2004.
 
25
26
27
 
28
I. Wegener. Simulated annealing beats metropolis in combinatorial optimization. In Proc. of ICALP '05, volume 3580 of LNCS, pages 589--601. Springer, 2005.
 
29
I. Wegener and T. Jansen. The analysis of evolutionary algorithms -- a proof that crossover really can help. Algorithmica, 34:47--66, 2002.


Collaborative Colleagues:
Benjamin Doerr: colleagues
Edda Happ: colleagues
Christian Klein: colleagues