7 Introduction to Optimisation
265
3. J. Knowles, D. Corne, Memetic Algorithms for Multiobjective Optimization: Issues, Methods
and Prospects (IEEE Press, Piscataway, 2000), pp. 325–332
4. J.D. Pintr, Global Optimization in Action (Springer, Berlin, 1996)
5. R. Horst, P.M. Pardalos, H.E. Romeijn, Handbook on Global Optimization: Nonconvex
Optimization and Its Applications (Springer, Berlin, 1995)
6. S. Rajasekaran, On simulated annealing and nested annealing. J. Glob. Optim. 16, 4356 (2000)
7. M. Locatelli, Simulated annealing algorithms for continuous global optimization. J. Optim.
Theory Appl. 104, 121–133 (2000)
8. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary
Programming, Genetic Algorithms (Oxford University Press, Oxford, 1996)
9. C.A. Floudas, Deterministic Global Optimization (Springer, Berlin, 2000)
10. P.M. Pardalos, Enumerative techniques for solving some nonconvex global optimization
problems. OR Spektr. 10, 29–35 (1988)
11. W. Forster, Homotopy Methods. Handbook of Global Optimization: Nonconvex Optimization
and Its Applications (Kluwer, Dordrecht, 1995), pp. 669–750
12. I. Diener, Trajectory Methods in Global Optimization. Handbook of Global Optimization:
Nonconvex Optimization and Its Applications (Kluwer, Dordrecht, 1995), pp. 649–668
13. R. Horst, H. Tuy, Global Optimization: Deterministic Approaches, 3rd edn. (Springer, Berlin,
1996)
14. T.W. Simpson, J. Peplinski, P.N. Koch, J.K. Allen, Metamodels for computer-based engineering design: survey and recommendations. Eng. Comput. 17(2), 129–150 (1995)
15. E.R. Hansen, G.W. Walster, Global Optimization Using Interval Analysis (CRC Press, Boca
Raton, 2003)
16. A.V. Levy, S. Gomez, The tunneling method applied to global optimization, in Numerical
Optimization (SIAM, Philadelphia, 1985), pp. 213–244
17. F.J. Solis, R.J.-B. Wets, Minimization by random search techniques. Math. Oper. Res. 6, 19–
30 (1981)
18. H.J. Kushner, A versatile stochastic model of a function of unknown and time varying form.
J. Math. Anal. Appl. 9, 379–388 (1962)
19. J. Mockus, On bayesian methods of optimization, in Toward Global Optimization, ed. by
L.C.W. Dixon, G.P. Szegö (North Holland, Amsterdam, 1975)
20. G. Rudolph, Convergence of evolutionary algorithms in general search spaces, in IEEE
International Conference on Evolutionary Computation (1996), pp. 50–54
21. G. Rudolph, Evolutionary search under partially ordered fitness sets, in International
Symposium on Information Science Innovations in Engineering of Natural and Artificial
Intelligent Systems (ISI 2001) (2001), pp. 818–822
22. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic methods for global optimization. Am. J. Math.
Manag. Sci. 4, 7–40 (1984)
23. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic global optimization methods, part I: clustering
methods. Math. Program. 39, 27–56 (1987)
24. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic global optimization methods, part II: multi
level methods. Math. Program. 39, 57–78 (1987)
25. J.M. Laarhoven Peter, H.L. Aarts Emile, Simulated Annealing (Springer, Berlin, 1987)
26. M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, 1998)
27. M. Sebag, A. Ducoulombier, Extending population-based incremental learning to continuous
search spaces, in 5th International Conference on Parallel Problem Solving from Nature
(PPSN V). Lecture Notes in Computer Science, vol. 1498 (Springer, Berlin, 1998), pp. 418–
427
28. P. Larrañaga, R. Etxeberria, J. Lozano, J. Peña, Optimization in continuous domains by
learning and simulation of Gaussian networks, in Conference on Genetic and Evolutionary
Computation (GECCO00) Workshop Program, pp. 201–204. (Morgan Kaufmann, San Mateo,
2000)
265
3. J. Knowles, D. Corne, Memetic Algorithms for Multiobjective Optimization: Issues, Methods
and Prospects (IEEE Press, Piscataway, 2000), pp. 325–332
4. J.D. Pintr, Global Optimization in Action (Springer, Berlin, 1996)
5. R. Horst, P.M. Pardalos, H.E. Romeijn, Handbook on Global Optimization: Nonconvex
Optimization and Its Applications (Springer, Berlin, 1995)
6. S. Rajasekaran, On simulated annealing and nested annealing. J. Glob. Optim. 16, 4356 (2000)
7. M. Locatelli, Simulated annealing algorithms for continuous global optimization. J. Optim.
Theory Appl. 104, 121–133 (2000)
8. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary
Programming, Genetic Algorithms (Oxford University Press, Oxford, 1996)
9. C.A. Floudas, Deterministic Global Optimization (Springer, Berlin, 2000)
10. P.M. Pardalos, Enumerative techniques for solving some nonconvex global optimization
problems. OR Spektr. 10, 29–35 (1988)
11. W. Forster, Homotopy Methods. Handbook of Global Optimization: Nonconvex Optimization
and Its Applications (Kluwer, Dordrecht, 1995), pp. 669–750
12. I. Diener, Trajectory Methods in Global Optimization. Handbook of Global Optimization:
Nonconvex Optimization and Its Applications (Kluwer, Dordrecht, 1995), pp. 649–668
13. R. Horst, H. Tuy, Global Optimization: Deterministic Approaches, 3rd edn. (Springer, Berlin,
1996)
14. T.W. Simpson, J. Peplinski, P.N. Koch, J.K. Allen, Metamodels for computer-based engineering design: survey and recommendations. Eng. Comput. 17(2), 129–150 (1995)
15. E.R. Hansen, G.W. Walster, Global Optimization Using Interval Analysis (CRC Press, Boca
Raton, 2003)
16. A.V. Levy, S. Gomez, The tunneling method applied to global optimization, in Numerical
Optimization (SIAM, Philadelphia, 1985), pp. 213–244
17. F.J. Solis, R.J.-B. Wets, Minimization by random search techniques. Math. Oper. Res. 6, 19–
30 (1981)
18. H.J. Kushner, A versatile stochastic model of a function of unknown and time varying form.
J. Math. Anal. Appl. 9, 379–388 (1962)
19. J. Mockus, On bayesian methods of optimization, in Toward Global Optimization, ed. by
L.C.W. Dixon, G.P. Szegö (North Holland, Amsterdam, 1975)
20. G. Rudolph, Convergence of evolutionary algorithms in general search spaces, in IEEE
International Conference on Evolutionary Computation (1996), pp. 50–54
21. G. Rudolph, Evolutionary search under partially ordered fitness sets, in International
Symposium on Information Science Innovations in Engineering of Natural and Artificial
Intelligent Systems (ISI 2001) (2001), pp. 818–822
22. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic methods for global optimization. Am. J. Math.
Manag. Sci. 4, 7–40 (1984)
23. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic global optimization methods, part I: clustering
methods. Math. Program. 39, 27–56 (1987)
24. A.H.G. Rinnooy Kan, G.T. Timmer, Stochastic global optimization methods, part II: multi
level methods. Math. Program. 39, 57–78 (1987)
25. J.M. Laarhoven Peter, H.L. Aarts Emile, Simulated Annealing (Springer, Berlin, 1987)
26. M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, 1998)
27. M. Sebag, A. Ducoulombier, Extending population-based incremental learning to continuous
search spaces, in 5th International Conference on Parallel Problem Solving from Nature
(PPSN V). Lecture Notes in Computer Science, vol. 1498 (Springer, Berlin, 1998), pp. 418–
427
28. P. Larrañaga, R. Etxeberria, J. Lozano, J. Peña, Optimization in continuous domains by
learning and simulation of Gaussian networks, in Conference on Genetic and Evolutionary
Computation (GECCO00) Workshop Program, pp. 201–204. (Morgan Kaufmann, San Mateo,
2000)