M. Emmerich, A. Giotis, M. Ozdemir, Th. Back, and K. Giannakoglou. Metamodelassisted evolution strategies. In Juan J. Merelo Guervos, Panagiotis Adamidis, HansGeorg Beyer, Jose Luis Fernandez-Villacanas Martn, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature VII, Proc. Int'l Conf., Granada 2002. Home/Search Document Details and Download
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A Comprehensive Survey of Fitness Approximation in Evolutionary.. - Jin (2003) (7 citations) (Correct)
....than do them randomly. However, the reduction of tness evaluations may not be signi cant. Use of approximate tness models through tness evaluations. In most research, the approximate model has been directly used in tness evaluations in order to reduce the number of tness calculation [44, 56, 57, 48, 64, 65, 20, 21, 51, 12, 59, 23, 36, 37, 38, 27, 22]. Di erent approximate models, including polynomials, kriging models and neural networks have been applied. An interesting idea in [22] is that a con dence interval for the tness estimation is calculated to modify the model prediction so that the search in unexplored regions is encouraged. It ....
....has been directly used in tness evaluations in order to reduce the number of tness calculation [44, 56, 57, 48, 64, 65, 20, 21, 51, 12, 59, 23, 36, 37, 38, 27, 22] Di erent approximate models, including polynomials, kriging models and neural networks have been applied. An interesting idea in [22] is that a con dence interval for the tness estimation is calculated to modify the model prediction so that the search in unexplored regions is encouraged. It has been found that strategy leads to better performance, especially when the original tness function is multimodal. Most recently, ....
M. Emmerich, A. Giotis, M. Ozdenir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In Parallel Problem Solving from Nature, number 2439 in Lecture Notes in Computer Science, pages 371-380. Springer, 2002.
Accelerating Evolutionary Algorithms Using Fitness.. - Büche, Schraudolph.. (Correct)
....(o#spring) can be (pre )evaluated by the model [3] The pre evaluation can be used to indicate promising solutions. It is far from clear however which of the new solutions should be evaluated on the objective function and the fraction of evaluated solutions varies in literature between 10 [4] and 50 [5] In addition, the inexact pre evaluation adds noise to the objective function, which might be detrimental to the optimization algorithm. In a second approach, the optimum is first searched on the model. The obtained optimum is then evaluated on the objective function and the result ....
Emmerich, M., Giotis, A., Ozdemir, M., Back, T., Giannakoglou, K.: Metamodel-assisted evolution strategies. In: Parallel Problem Solving from Nature - PPSN VII, Springer-Verlag (2002) 361--370
Metamodel-Assisted Evolution STrategies Applied in . . . - Varcol (2005) Self-citation (Emmerich) (Correct)
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M. Emmerich, A. Giotis, M. Ozdemir, Th. Back, and K. Giannakoglou. Metamodelassisted evolution strategies. In Juan J. Merelo Guervos, Panagiotis Adamidis, HansGeorg Beyer, Jose Luis Fernandez-Villacanas Martn, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature VII, Proc. Int'l Conf., Granada 2002.
Single- and Multi-objective Evolutionary Optimization .. - Emmerich.. Self-citation (Emmerich Giannakoglou) (Correct)
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M. Emmerich, A. Giotis, M. Ozdemir, T. Back, and K. Giannakoglou, "Metamodel-assisted evolution strategies," in Parallel Problem Solving from Nature VII, Proc. Int'l Conf., Granada 2002.
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M. Emmerich, A. Giotis, M. Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In J. J. Merelo Guervos et al., editor, Parallel Problem Solving from Nature -- PPSN VII, Proc. Seventh Int'l Conf., Granada, pages 361--370, Berlin, 2002. Springer.
Optimization by Gaussian Processes assisted - Evolution Strategies Holger (2003) (Correct)
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M. Emmerich, A. Giotis, M.Multlu Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In Parallel Problem Solving from Nature VII, pages 362--370, 2002.
Evolution Strategies assisted by Gaussian Processes - With Improved Pre-Selection (2003) (Correct)
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M. Emmerich, A. Giotis, M.Multlu Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In Parallel Problem Solving from Nature VII, pages 362--370, 2002.
Tuning Search Algorithms for Real-World Applications: A.. - Bartz-Beielstein, Markon (Correct)
No context found.
M. Emmerich, A. Giotis, M. Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In J. J. Merelo Guervos et al., editor, Parallel Problem Solving from Nature -- PPSN VII, Proc. Seventh Int'l Conf., Granada, pages 361--370, Berlin, 2002. Springer.
Evolution Strategies assisted by Gaussian Processes with.. - Ulmer, Streichert, Zell (2003) (2 citations) (Correct)
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M. Emmerich, A. Giotis, M.Multlu Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In Parallel Problem Solving from Nature VII, pages 362--370, 2002.
Accelerating Evolutionary Algorithms with Gaussian.. - Büche, Schraudolph, .. (2004) (Correct)
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M. Emmerich, A. Giotis, M. Ozdemir, T. B ack, and K. Giannakoglou, "Metamodel-assisted evolution strategies," in Parallel Problem Solving from Nature - PPSN VII. Springer-Verlag, 2002, pp. 361--370.
Tuning Search Algorithms for Real-World Applications: A.. - Bartz-Beielstein, Markon (2004) (Correct)
No context found.
M. Emmerich, A. Giotis, M. Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In J. J. Merelo Guervos et al., editor, Parallel Problem Solving from Nature -- PPSN VII, Proc. Seventh Int'l Conf., Granada, pages 361--370, Berlin, 2002. Springer.
Optimization by Gaussian Processes assisted Evolution.. - Ulmer, Streichert, Zell (2003) (Correct)
No context found.
M. Emmerich, A. Giotis, M.Multlu Ozdemir, T. Back, and K. Giannakoglou. Metamodel-assisted evolution strategies. In Parallel Problem Solving from Nature VII, pages 362--370, 2002.
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