A comparison of simulated annealing with a simple evolutionary algorithm

Lecture Notes in Computer Science

Volumn 3469, Issue , 2005, Pages 37-57

  Jansen, Thomas ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN FUNCTIONS; COMPUTER SCIENCE; COMPUTER SYSTEMS PROGRAMMING; EVOLUTIONARY ALGORITHMS; HEURISTIC METHODS;

HEURISTICS; LIMITATIONS; PSEUDO-BOOLEAN FUNCTIONS;

SIMULATED ANNEALING;

EID: 24944560620     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11513575_3     Document Type: Conference Paper

References (20)

1. T. Back (1996): Evolutionary Algorithms in Theory and Practice. Oxford University Press.
2. T. Carson, R. Impagliazzo (2001): Hill-climbing finds random planted bisections. Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA 2001). 903-909.
3. T. Cormen, C. Leiserson, R. Rivest, C. Stein (2001): Introduction to Algorithms. MIT Press.
4. S. Droste, T. Jansen, I. Wegener (2001): Dynamic parameter control in simple evolutionary algorithms. In W. N. Martin, W. M. Spears (Eds.): Foundations of Genetic Algorithms 6 (FOGA 2000). Morgan Kaufmann. 275-294.
5. S. Droste, T. Jansen, I. Wegener (2002): On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276:51-81.
6. J. Garnier, L. Kallel, M. Schoenauer (1999): Rigorous hitting times for binary mutations. Evolutionary Computation 7(2):173-203.
7. W. E. Hart (1995): A theoretical comparison of evolutionary algorithms and simulated annealing. In Proceedings of the Fifth Annual Conference on Evolutionary Programming 147-154.
8. L. Ingber, B. Rosen (1992): Genetic algorithms and very fast reannealing: a comparison. Mathematical and Computer Modeling 16:87-100.
9. T. Jansen, I. Wegener (2000): On the choice of the mutation probability for the (1+1) EA. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, H.-P. Schwefel (Eds.): Sixth International Conference on Parallel Problem Solving From Nature (PPSN VI). Springer, LNCS 1917, Springer. 89-98.
10. T. Jansen, I. Wegener (2002): On the analysis of evolutionary algorithms - a proof that crossover really can help. Algorithmica 34(1):47-66.
11. M. R. Jerrum, G. Sorkin (1993): Simulated annealing for graph bisection. Proceedings of the IEEE Symposium on Foundations of Computer Science (FOGS '93). IEEE Press. 94-103.
12. S. Kirkpatrick, C. D. Gelatt, M.P. Vecchi (1983): Optimization by simulated annealing. Science 220, 671-680.
13. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller (1953): Equation of state calculation by fast computing machines. Journal of Chemical Physics 21, 1078-1092.
14. H. Mühlenbein (1992): How genetic algorithms really work. Mutation and Hill-climbing. In Männer, Manderick (Eds): Proceedings of the 2nd Parallel Problem Solving from Nature (PPSN II). North-Holland. 15-25.
15. A. Prügel-Bennett (2004): When a genetic algorithm outperforms hill-climbing. Theoretical Computer Science 320(1): 135-153.
16. G. Rudolph (1997): Convergence Properties of Evolutionary Algorithms. Dr. Kovač.
17. G.B. Sorkin (1991): Efficient simulated annealing on fractal energy landscapes. Algorithmica 6, 346-418.
18. N.L. J. Ulder, E. H.L. Aarts, H.-J. Bandelt, P. J. M. van Laarhoven, E. Pesch. (1994): Genetic local search algorithms for the traveling salesman problem. In First International Conference on Parallel Problem Solving from Nature (PPSN I), 109-116.
19. I. Wegener (2004): Simulated annealing beats Metropolis in combinatorial optimization. Technical Report SFB 531, CI 181/04. University of Dortmund, Germany.
20. I. Wegener, C. Witt (2003): On the optimization of monotone polynomials by the (1+1) EA and randomized local search. In Genetic and Evolutionary Computation Conference (GECCO 2003), Springer. 622-633.