An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization

Computers in Industry

Volumn 58, Issue 3, 2007, Pages 279-291

  Brintrup, Alexandra Melike ^a   Ramsden, Jeremy ^a   Tiwari, Ashutosh ^a  

^a CRANFIELD UNIVERSITY   (United Kingdom)

Author keywords

Design optimization; Interactive evolutionary computation; Manufacturing plant layout design; Multi objective optimization; Qualitativeness

Indexed keywords

COMPUTATION THEORY; OPTIMIZATION; PLANT LAYOUT;

INTERACTIVE EVOLUTIONARY COMPUTATION; MANUFACTURING PLANT LAYOUT DESIGN; MULTI-OBJECTIVE OPTIMIZATION; QUALITATIVENESS;

GENETIC ALGORITHMS;

EID: 33847108964     PISSN: 01663615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compind.2006.06.004     Document Type: Article

References (17)

1. I.C. Parmee, D. Cvetkovic, A. Watson, C. Bonham, Multi-objective satisfaction within an interactive evolutionary design environment, Journal of Evolutionary Computation, MIT Press 8 (2) (2000) 197-222.
2. Shibuya M., Kita H., and Kobayashi S. Integration of multi-objective and interactive genetic algorithms and its application to animation design. Proceedings of the IEEE Systems, Man and Cybernetics (SMC), vol. 3. Tokyo, Japan (1999) 646-651
3. Oduguwa V., Tiwari A., and Roy R. Handling integrated quantitative and qualitative search space in a real world optimization problem. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). Canberra, Australia (2003) 1222-1229
4. Kamalian R., Takagi H., and Agogino A.M. Optimized design of MEMS by evolutionary multi-objective optimization with interactive evolutionary computation. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). Seattle, US (2004) 1030-1041
5. Parmee I.C. Evolutionary and Adaptive Computing in Engineering Design (2001), Springer, Berlin pp. 204-231
6. Parmee I.C., and Bonham C.R. Cluster-oriented genetic algorithms to support interactive designer/evolutionary computing systems. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). Washington, DC, US (1999) 546-553
7. Deb K. Multi-objective Optimization Using Evolutionary Algorithms (2001), Wiley, New York
8. Goldberg D.E. Genetic Algorithms in Search Optimization and Machine Learning (1989), Addison Wesley, Toronto
9. Mitchell M. An Introduction to Genetic Algorithms (1998), MIT Press, Cambridge, MA
10. Takagi H. Interactive evolutionary computation: fusion of the capabilities of EC computation and human evaluations. Proceedings of the IEEE 89 9 (2001) 1275-1296
11. Parmee I.C. Poor-definition, uncertainty and human factors-a case for interactive evolutionary problem reformulation?. Proceedings of the 3rd IEC Workshop of the Genetic and Evolutionary Computation Conference (GECCO) (2003)
12. Deb K., Agrawal S., Pratap A., and Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi objective optimization: NSGA-2. Proceedings of the Parallel Problem Solving from Nature (PPSN). Paris, France (2000) 858-862
13. Wang S., and Takagi H. Evaluation of user fatigue reduction through IEC rating-scale mapping. Proceedings of the Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology. Muroran, Japan, May 25-27 (2005)
14. Wang L., Wei P., and Chang Y. Reducing evaluation fatigue in interactive, evolutionary algorithms by using an incremental learning approach. Proceedings of the Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology. Muroran, Japan, May 25-27 (2005)
15. L.A. Zadeh, A Theory of Approximate Reasoning, Journal of Machine Intelligence, Halstead, New York, 9 (1979) pp. 149-194.
16. Jain K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Computing Surveys 31 (1999) 265-322
17. Nagao M., Yamamoto M., Suzuki K., and Ohuchi A. Evaluation of the image retrieval system using interactive genetic algorithm. Journal of Japanese Society for Artificial Intelligence 13 5 (1998) 720-727