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34. Deb, K., Georg Beyer, H.: Self-adaptive genetic algorithms with simulated binary
crossover. Evolutionary Computation 9(2), 197-221 (2001)
35. Eiben, A., Schut, M.C., de Wilde, A.: Is self-adaptation of selection pressure and
population size possible? - a case study. In: Proceedings of the 9th Conference on
Parallel Problem Solving from Nature - PPSN IX, pp. 900-909 (2006)
36. Eiben, A.E., Aarts, E.H.L., van Hee, K.M.: Global convergence of genetic algo-
rithms: A markov chain analysis. In: PPSN I: Proceedings of the 1st Workshop
on Parallel Problem Solving from Nature, pp. 4-12. Springer, Berlin (1991)
37. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolution-
ary algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124-141
(1999)
38. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer,
Berlin (2003)
39. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-
schemata. In: Proceedings of the FOGA, pp. 187-202 (1992)
40. Fiacco, A., McCormick, G.: The sequential unconstrained minimization technique
for nonlinear programming - a primal-dual method. Mgmt. Sci. 10, 360-366 (1964)
41. Fogarty, T.C.: Varying the probability of mutation in the genetic algorithm. In:
Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 104-
109. Morgan Kaufmann, San Francisco (1989)
42. Fogel, D.B.: Evolving Artificial Intelligence. PhD thesis, University of California,
San Diego (1992)
43. Fogel, D.B., Fogel, L.J., Atma, J.W.: Meta-evolutionary programming. In: Pro-
ceedings of 25th Asilomar Conference on Signals, Systems & Computers, pp.
540-545 (1991)
44. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated
Evolution. Wiley, New York (1966)
45. Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory
of NP-Completeness. W. H. Freeman & Co., New York (1990)
46. Glickman, M., Sycara, K.: Reasons for premature convergence of self-adapting
mutation rates. In: Proceedings of the Congress on Evolutionary Computation,
vol. 1, pp. 62-69 (July 2000)
47. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning.
Addison-Wesley, Reading (1989)
48. Goldberg, D.E., Lingle, R.: Alleles, loci and the traveling salesman problem. In:
Grefenstette (ed.) Proceedings of the 1st International Conference on Genetic
Algorithms and Their Applications, pp. 154-159 (1985)
49. Grahl, J., Bosman, P.A.N., Minner, S.: Convergence phases, variance trajectories,
and runtime analysis of continuous edas. In: Proceedings of the 9th conference on
genetic and evolutionary computation - GECCO, pp. 516-522. ACM Press, New
York (2007)
50. Grahl, J., Minner, S., Rothlauf, F.: Behaviour of umdac with truncation selection
on monotonous functions. In: Proceedings of the IEEE Congress on Evolutionary
Computation - CEC, vol. 3, pp. 2553-2559 (2005)
51. Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE
Trans. Syst. Man Cybern. 16(1), 122-128 (1986)
52. Hansen, N.: The cma evolution strategy: A tutorial. Technical report, TU Berlin,
ETH Zurich (2005)
53. Hansen, N.: An analysis of mutative sigma self-adaptation on linear fitness func-
tions. Evolutionary Computation 14(3), 255-275 (2006)
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