A fitness differential adaptive parameter controlled evolutionary algorithm with application to the design structure matrix

International Journal of Production Research

Volumn 46, Issue 18, 2008, Pages 5043-5057

  Lancaster, John ^a   Cheng, Kai ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Design structure matrix; Differential adaptation; Evolutionary algorithms

Indexed keywords

ALGORITHMS; BENCHMARKING; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; GENETIC ALGORITHMS; LARGE SCALE SYSTEMS; MARINE ENGINEERING; MATRIX ALGEBRA; PROBABILITY; RANDOM PROCESSES; RISK ASSESSMENT; THREE TERM CONTROL SYSTEMS;

DESIGN STRUCTURE MATRIX; DIFFERENTIAL ADAPTATION; MUTATION PROBABILITY;

EVOLUTIONARY ALGORITHMS;

EID: 51749101482     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207540701324176     Document Type: Article

References (17)

1. Eiben, A., Hinterding, R. and Michalewicz, Z., Parameter control in evolutionary algorithms. IEEE Trans. Evolut. Comput., 1999, 3, 124-141.
2. Goldberg, D., Genetic Algorithms in Search, Optimisation and Machine Learning, 1989 (Addison Wesley Longman Inc.).
3. Kolisch, R. and Sprecher, A., PSPLIB: A project scheduling problem library, Eur. J. Oper. Res., 1997, 96, 205-216 [Also available through Christian Albrechts Universitat zu Kiel, Germany online at: http://129.187.106.231/psplib/].
4. Lancaster, J. and Ozbayrak, M., Evolutionary algorithms applied to project scheduling problems - a survey of the state-of-the-art. Int. J. Prod. Res., 2007, 45, 425-450.
5. Leu, S. and Yang, C., A GA-based multicriteria optimal model for construction scheduling. J. Constr. Eng. Manag., 1999, 125, 420-427.
6. Murata, T., Genetic algorithms for multi-objective optimization. Doctoral thesis, Osaka Prelecture University, 1997.
7. Murata, T. and Ishibuchi, H., Performance evaluation of genetic algorithms for flowshop scheduling problems. Proceedings of the first IEEE Conference on Evolutionary Computation., 1994, 2, 812-817.
8. Rogers, J., Ordering design tasks based on coupling strength, in 5th AIAA/NASA/USAF/ ISSMO Symposium on Multidisciplinary Analysis and Optimisation, 1994.
9. Rogers, J., Integrating a Genetic Algorithm into a Knowledge-Based System for Ordering Complex Design Processes. Technical report: NASA-96-icai.jlr, NASA Langley Research Centre, Hampton, Virginia, 1996.
10. Sewell, M., Samarabandu, J., Rodrigo, R. and McIsaac, K., The rank-scaled mutation rate for genetic algorithms. Int. J. Inf. Tech., 2006, 3, 32-36.
11. Starkweather, T., McDaniel, S., Mathias, K., Whitely, D. and Whitely, C., A comparision of genetic sequencing operation. Proceedings of the third International Conference on Genetic algorithms., 1991, 69-76.
12. Thierens, D., Adaptive Mutation Rate Control Schemes in Genetic Algorithms. Technical report: UU-CS-2002-056, Institute of Information and Computing Sciences, Utrecht University, 2002.
13. Todd, D., Multiple criteria genetic algorithms in engineering design and operation. PhD thesis, Department of Marine Technology, University of Newcastle, 1997.
14. Ursem, R., Models of evolutionary algorithms and their applications in systems identification and control optimisation. PhD thesis, Faculty of Science, University of Aarhus, 2003.
15. Whitfield, R., Duffy, A., Coates, G. and Hills, W., Efficient process optimisation. Concurr. Eng., 2003, 11, 83-92.
16. Younes, M. and Rahli, M., On the choice of genetic parameters with Taguchi method applied in economic power dispatch. Leonardo J. Sci., 2006, 9, 9-24.
17. Zhuang, M. and Yassine, A., Task Scheduling of Parallel Development Projects using Genetic Algorithms, in DETC'04 ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Salt Lake City, Utah USA, 2004.