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References
D. E. Goldberg and R. E. Smith. Nonstationary function optimization using genetic algorithms with dominance and diploidy. In J. J. Grefenstette, editor, Second International Conference on Genetic Algorithms, pages 59-68. Lawrence Erlbaum Associates, 1987.
T. Back (1998). On the behavior of evolutionary algorithms in dynamic fitness landscapes. In Proc. of the 1998 IEEE Int. Conf. on Evolutionary Computation, pp 446-451. IEEE Press.
N. Mori, S. Imanishi, H. Kita, and Y. Nishikawa. Adaptation to changing envi-ronments by means of the memory based thermodynamical genetic algorithm. In T. Bäck, editor. Seventh International Conference on Genetic Algorithms, pp 299-306. Morgan Kaufmann, 1997.
J. Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, 2002.
J. Branke. Evolutionary approaches to dynamic optimization problems - Instruction and recent trends-. in J. Branke, editor, Proceedings of the Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp 1-3. Chicago, USA, 2003.
H. G. Cobb. An investigation into the use of hypermutation as adaptive op-erator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA, 1990.
R. W. Morrison and K. A. De Jong (2000). Triggered hypermutation revisited. Proc. of the 2000 Congress on Evolutionary Computation, pp 1025-1032.
J. J. Grefenstette. Genetic algorithms for changing environments. In R. Maenner and B. Manderick, editors, Parallel Problem Solving from Nature 2, pp 137-144. North-Holland, 1992.
J. Lewis, E. Hart and G. Ritchie (1998). A comparison of dominance mecha-nisms and simple mutation on non-stationary problems. Proc. of the 5th Int. Conf. on Parallel Problem Solving from Nature, pp 139-148.
K. P. Ng and K. C. Wong (1995). A new diploids scheme and dominance change mechanism for non-stationary function optimisation. In L. J. Eshelman (ed.), Proc. of the 6th Int. Conf. on Genetic Algorithms.
S. Yang. Memory-based immigrants for genetic algorithms in dynamic environments. Proceedings of the 2005 Genetic and Evolutionary Computation Con-ference, Vol. 2, pp 1115-1122, 2005.
J. Branke, T. Kaufler, C. Schmidt, and H. Schmeck. A multipopulation ap-proach to dynamic optimization problems. Adaptive Computing in Design and Manufacturing. Springer, 2000.
D. C. Montgomery, Design and Analysis of Experiments, 3rd ed. New York: Wiley, 1991.
S. Zeng, L. Kang, L. Ding. An Orthogonal Multi-objective Evolutionary Algo-rithm for Multi-objective Optimization Problems with Constraints. Evolutionary Computation. Vol.12, No.1, pp 77-98, MIT Press, 2004.
S. Zeng, S. Yao, L. Kang, and Y. Liu. An Efficient Multi-objective Evolutionary Algorithm: OMOEA-II. In C. A. Coello Coello et al. (Eds.), proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, LNCS series, Springer-Verlag, pp 108-119, 2005.
Y. W. Leung and Q. Zhang (1997). Evolutionary algorithms + experimental design methods: A hybrid approach for hard optimization and search problems, Res. Grant Proposal, Hong Kong Baptist Univ.
Y. W. Leung and Y. Wang (2001). An orthogonal genetic algorithm with quan-tization for global numerical optimization. IEEE Trans. Evol. Comput. vol.5, No.1, pp. 40-53.
T. C. Fogarty, F. Vavak, and P. Cheng. Use of the genetic algotithm for load balancing of sugar beet presses. Proceedings of the 6th Int. Conf. on Genetic Algorithms, pages 617-624. Morgan Kanufmann, 1995.
C. L. Karr. Genetic algorithms and fuzzy logic for adaptive process control. In S. Goonatilake and S. Khebbal, editors, Intelligeht Hybrid Systems, chapter 4, pages 63-64, John Wiley, 1995.
C. L. Karr. Adaptive process control using biologic paradigms, In L. C. Jain, editor, Proceedings of Electronic Technology Directions to the Year 2000, volume 1, pages 128-136, 1995. 104 Sanyou Zeng, Hui Shi, Lishan Kang, and Lixin Ding
C. L. Ramsey and J. J. Grefenstette. Case-based initialization of genetic algo-rithms, In S. Forrest, editor, Proceedings of the 5th International Conference on Genetic Algorithms, pages 84-91. Morgan Kaufmann, 1993.
R. W. Morrison and K. A. DeJong. A test problem generator for nonstationary environments. Proceedings of the 1999 Congress on Evolutionary Computation, volume 3, pages 2047-2053, 1999.
S. Yang. Constructing dynamic test environments for genetic algorithms based on problem difficulty. Proceedings of the 2004 Congress on Evolutionary Com-putation, Vol. 2, pages 1262-1269, 2004.
A. S. Hedayat, N. J. A. Sloane and J. Stufken. Orthogonal Arrays: Theory and Applications. New York: Springer-Verlag, 1999.
J. Branke, T. Kaubler, and H. Schmeck. Guiding multiobjective evolutionary algorithms towards interesting regions. Technical Report No. 399, Institute AIFB, University of Karlsruhe, Germany, 2000
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Zeng, S., Shi, H., Kang, L., Ding, L. (2007). Orthogonal Dynamic Hill Climbing Algorithm: ODHC. In: Yang, S., Ong, YS., Jin, Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49774-5_4
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