An evolutionary, agent-assisted strategy for conceptual design space decomposition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1305, Issue , 1997, Pages 275-286

  Parmee, I C ^a   Beck, M A ^a  

^a UNIVERSITY OF PLYMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; ADAPTIVE FILTERS; DECISION MAKING; GENETIC ALGORITHMS;

APRIORI KNOWLEDGE; DECISION MAKING PROCESS; DESIGN CHARACTERISTICS; EXTRACTION OF INFORMATION; OPTIMAL DESIGN; PERFORMANCE REGIONS; RAPID DECOMPOSITION; SEARCH PROCESS;

CONCEPTUAL DESIGN;

EID: 84949198807     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0027181     Document Type: Conference Paper

References (15)

1. Parmee I. C., Denham M. J. (1994) The Integration of Adaptive Search Techniques with Current Engineering Design Practice. Procs. of Adaptive Computing in Engineering Design and Control; University of Plymouth, UK; Sept. 1994; pp 1-13
2. Parmee I. C. (1997) Evolutionary. and Adaptive Strategies for Engineering Design - an Overall Framework. IEEE International Conference on Evolutionary Computation, April 13-16, 1997.
3. Parmee I. C, (1996) Towards an Optimal Engfineering Design Process Using Adaptive Search Strategies. Journal of Engineenng Design 7(4). pp 341-362.
4. Jarvis IL A. and Patrick, E. A. (1993) Clustering using a Similarity Measure Based on Shared Near Neighbours. IEEE Transactions on Computers, (22) 11.
5. Parmee I. C. (1996) Cluster-Oriented Genetic Algorithms (COGA's) for the Identification of High-Performance Regions of Design Space. Procs. EvCA Conference Moscow. June 24-27 1996
6. Reeves, C. R (1993) Using Genetic Algorithms with Small Populations. Procs. of the Fifth International Conference on Genetic Algorithms. pp 92-99
7. Brooker, L. (1987) Improving Search in Genetic Algorithms, In Davis, L. (Ed) Genetic Algorithms and Simulated Annealing pp 61-73. Morgan Kaufinann
8. Baker, J. E., (1987) Reducing Bias and Inefficiency in the Selection Algorithm. Procs of the Second International Conference on Genetic Algorithms, pp 14-21
9. Lin, S. C. and Goodman, E. (1994) Coarse-gram parallel genetic algorithms: Categorisation new approach. Sixth IEEE symposium on Parallel and Distributed Processing, Los Alamitos, CA: IEEE Computer Society Press
10. Syswerda, G. (1989) A study of reproduction in generational and steady state genetic algorithms. In G. Rawlings (Ed.), Foundation of Genetic Algorithms, Morgan Kaufman.
11. Davidor, Y., Yamada T. & Nakano, II (I993) The ECOlogical framework II: Improving GA perfonmnce at virtually zero cost. Procs of the Fifth International Conference on genetic Algorithms, pp 171–175
12. Spiessens, P & Manderick, B. (1991) A Massively Parallel Genetic Algorithm: Implementation and F'xst Analysis. Procs. of the Fourth International Conference on Genetic Algorithms. pp 257–263
13. Beck, M. A. (1996) Parallel Genetic Algorithms: An Investigation of Coarse and Free Grained approaches to Design Space Decomposition. MSc Thesis. University of Plymouth.
14. Roy R., Parmee I. C., Purchase, G. (1996) Integrating the Genetic Algorithm with the Preliminary Design of Gas Turbine Blade Cooling Systems. Procs. of Adaptive Computing in Engineering Design and Control; University of Plymouth, UK; March. 1996.
15. Parmee I. C., (1996) The Development of a Dual-Agent Strategy for Efficient Search Across Whole System Engineering Design Hierarchies. Parallel Problem Solving From Nature, Lecture Notes in Computer Science, September 22-27 1996