An adaptive optimization technique for dynamic environments

Engineering Applications of Artificial Intelligence

Volumn 23, Issue 5, 2010, Pages 772-779

  Liua, Li ^a   Ranjithanb, S Ranji ^b  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b North Carolina State University   (United States)

Author keywords

Adaptability; Adaptive dynamic optimization; Contaminant source identification; Diversity; Evolutionary algorithms

Indexed keywords

EVOLUTIONARY ALGORITHMS; GROUNDWATER; GROUNDWATER POLLUTION; OPTIMAL SYSTEMS; OPTIMIZATION;

ADAPTABILITY; ADAPTIVE DYNAMICS; CONTAMINANT SOURCE IDENTIFICATIONS; DIVERSITY; EVOLUTIONARY ALGORITHMS (EAS); GROUNDWATER CONTAMINANTS; INDIVIDUAL DIVERSITIES; OPTIMIZATION PROBLEMS;

CLUSTERING ALGORITHMS;

EID: 79952347115     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.01.007     Document Type: Article

References (22)

1. Branke, J., 1999. Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1875-1882.
2. Branke, J., 2002. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Boston.
3. Bui, L.T., Branke, J., Abbass, H.A., 2005a. Diversity as a selection pressure in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1557-1558. (Pubitemid 43226480)
4. Bui, L.T., Branke, J., Abbass, H.A., 2005. Multiobjective Optimization for Dynamic Environments. In: Proceedings of the Congress on Evolutionary Computation. IEEE Press, Edinburgh, UK, pp. 2349-2356. (Pubitemid 41496141)
5. Eriksson, R., Olsson, B., 2002. On the behaviour of evolutionary globallocal hybrids with dynamic fitness functions. Parrallel Problem Solving From Nature VII, 13-22.
6. Eriksson, R., Olsson, B., 2004. On the performance of evolutionary algorithms with life-time adaptation in dynamic fitness landscapes. In: Proceedings of the 2004 congress on evolutionary computation, pp. 1293-1300.
7. Ghosh, A., Tstutsui, S., Tanaka, H., 1998. Function optimization in nonstationary environment using steady state Genetic Algorithms with aging of individuals. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 667-671.
8. Goldberg, D.E., Richardson, J., 1987. Genetic Algorithms with sharing for multi- modal function optimization. In: Proceedings of the 2nd International Conference on Genetic Algorithms, pp. 41-49.
9. Goldberg, D.E., Smith, R.E., 1987. Nonstationary function optimization using Genetic Algorithms with dominance and diploidy. In: Proceedings of the 2nd International Conference on Genetic Algorithms, pp. 59-68.
10. Grefenstette, J.J., 1992. Genetic Algorithms for changing environments. In: Maenner, R., Manderick, B. (Eds.), Parallel Problem Solving from Nature, pp. 137-144.
11. Hoffmeister, F., Back, T., 1992. Genetic self-learning. In: Proceedings of the 1st European Conference on Artificial Life, pp. 227-235.
12. Holland, J.H., 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.
13. Oppacher, F., Wineberg, M., 1999. The shifting balance Genetic Algorithm: improving the GA in a dynamic environment. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 504-510.
14. Ursem, R.K., 2000. Multinational gas: multimodal optimization techniques in dynamic evironments. In: Proceedings of the Second Genetic and Evolutionary Computation Conference.
15. Wang, H., Wang, D., Yang, S., 2007. Triggered memory-based swarm optimization in dynamic environments. Applications of Evolutionary Computing, LNCS 4448, 637-646. (Pubitemid 47290122)
16. William, E.H., Krasnogor, N., Smith, J.E. (Eds.), 2005. Recent Advances in Memetic Algorithms. Springer, Berlin.
17. Yang, S., 2005a. Memory-based immigrants for Genetic Algorithms in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1115-1122. (Pubitemid 43226422)
18. Yang, S., 2005b. Population-based incremental learning with memory scheme for changing environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 711-718. (Pubitemid 43226364)
19. Yang, S., Ong, Y., Jin, Y. (Eds.), 2007. Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Berlin, London.
20. Yang, S., Yao, X., 2008. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation 12 (5), 542-561.
21. Zechman, E.M., Ranjithan, S., 2004. An Evolutionary Algorithm to Generate Alternatives (EAGA) for engineering optimization problems. Engineering Optimization 36 (5), 539-553.
22. Zechman, E.M., Ranjithan, S., 2007. Evolutionary computation-based approach for model error correction and calibration. Advances in Water Resources 30 (5), 1360-1370. (Pubitemid 46323444)