Intelligent mutation rate control in canonical genetic algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1079, Issue , 1996, Pages 158-167

  Bäck, Thomas ^a   Schiitz, Martin ^b  

^a Center for Applied Systems Analysis (CASA)   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL OPTIMIZATION; INTELLIGENT SYSTEMS;

COMBINATORIAL OPTIMIZATION PROBLEMS; CONTROL MECHANISM; EVOLUTION STRATEGIES; INTELLIGENT MUTATION; MAXIMUM INDEPENDENT SETS; OPTIMIZATION PROBLEMS; SELF ADAPTATION; SELF-ADAPTATION MECHANISMS;

GENETIC ALGORITHMS;

EID: 84949806686     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61286-6_141     Document Type: Conference Paper

References (26)

1. Th. Bäck. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In R. Männer and B. Manderick, editors, Parallel Problem Solving front Nature, 2, pages 85-94. Elsevier, Amsterdam, 1992.
2. Th. Bäck. Self-Adaptation in Genetic Algorithms. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life, pages 263-271. The MIT Press, cambridge, MA, 1992.
3. Th. Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, 1996.
4. Th. Bäck and S. Khuri. An evolutionary heuristic for the maximum independent set problem. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 531-535. IEEE Press, 1994.
5. Th. Bs and M. Schütz. Evolution strategies for mixed-integer optimization of optical multilayer systems. In J. R. McDonnell, R. G. Reynolds, and D. B. Fogel, editors, Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming, pages 33-51. MIT Press, Cambridge, MA, 1995.
6. Th. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1-23, 1993.
7. L. J. Eshelman, R. A. Caruna, and J. D. Schaffer. Biases in the crossover landscape. In J. D. Schaffer, editor, Proceedings of the 3rd International Conference on Genetic Algorithms, pages 10-19. Morgan Kaufmann Publishers, San Mateo, CA, 1989.
8. D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, N J, 1995.
9. D. B. Fogel, L. J. Fogel, and W. Atmar. Meta-evolutionary programming. In R. R. Chen, editor, Proc. 25th Asilomar Conference on Signals, Systems and Computers, pages 540-545. Pacific Grove, CA, 1991.
10. L. Fogel, D. B. Fogel, and P. J. Angeline. A preliminary investigation on extending evolutionary programming to include self-adaptation on finite state machines, lnformatica, 18:387-398, 1994.
11. D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, MA, 1989.
12. J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-16(1):122-128, 1986.
13. F. Hoffmeister and Th. Bäck. Genetic self-learning. In F.J. Varela and P. Bourgine, editors, Proceedings of the 1st European Conference on Artificial Life, pages 227-235. The MIT Press, Cambridge, MA, 1992.
14. J. H. Holland. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI, 1975.
15. K. A. De Jong. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975.
16. S. Khuri and Th. Bäck. An evolutionary heuristic for the minimum vertex cover problem. In J. Kunze and H. Stoyan, editors, K1-94 Workshops (Extended Abstracts), pages 83-84. Gesellschaft für Informatik e. V., Bonn, 1994.
17. S. Khuri, Th. Bäck, and J. Heitkötter. An evolutionary approach to combinatorial optimization problems. In D. Cizmar, editor, Proceedings of the 22nd Annual ACM Computer Science Conference, pages 66-73. ACM Press, New York, 1994.
18. S. Khuri, Th. Bgck, and J. Heitkötter. The zero/one multiple knapsack problem and genetic algorithms. In E. Denton, D. Oppenheim, J. Urban, and H. Berghel, editors, Proceedings of the 1994 ACM Symposium on Applied Computing, pages 188-193, ACM Press, New York, 1994.
19. H. Mühlenbein. How genetic algorithms really work: I. mutation and hillclimbing. In R. Männer and B. Manderick, editors, Parallel Problem Solving from Nature 2, pages 15-25. Elsevier, Amsterdam, 1992.
20. J. Obalek. Rekombinationsoperatoren für Evolutionsstrategien. Diplomarbeit, Universität Dortmund, Fachbereich Inforatatik, 1994.
21. J. D. Schaffer, R. A. Caruana, L. J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In J. D. Schaffer, editor, Proceedings of the 3rd International Conference on Genetic Algorithms, pages 51-60. Morgan Kanfmann Publishers, San Mateo, CA, 1989.
22. J. D. Schaffer and A. Morishima. An adaptive crossover distribution mechanism for genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the 2nd International Conference on Genetic Algorithms and Their Applications, pages 36-40. Lawrence Erlbanm Associates, Hillsdale, N J, 1987.
23. H.-P. Schwefel. Collective intelligence in evolving systems. In W. Wolff, C.-J. Soeder, and F. R. Drepper, editors, Ecodynamics, Contributions to Theoretical Ecology, pages 95-100. Springer, Berlin, 1987.
24. H.-P. Schwefel. Imitating evolution: Collective, two-level learning processes. In U. Witt, editor, Explaining Process and Change -- Approaches to Evolutionary Economics, pages 49-63. The University of Michigan Press, Ann Arbor, MI, 1992.
25. H.-P. Schwefel. Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series. Wiley, New York, 1995.
26. H. M. Weingartner and D.N. Ness. Methods for the solution of the multidimensional 0/1 knapsack problem. Operations Research, 15:83-103, 1967.