Advertisement

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

  • I. C. Parmee
  • M. A. Beck
Novel Techniques and Applications of Evolutionary algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

Genetic algorithm based strategies for the rapid decomposition of complex, conceptual design spaces into discrete, bounded regions of high performance are described The objective is not to identify single peaks but to identify high performance regions. Sufficient regional cover (in terns of number of solutions) is required for the extraction of infomnation relating to design characteristics to support the designer in decision making processes for the definition of optimal design direction. Three strategies using single population and parallel GA implemertations are described and results are presented An adaptive filter is introduced to eliminate a need for apron knowledge of the design space. Rule-based agents complement the search process by exploring identified regions to define region bounds.

Keywords

Genetic Algorithm Design Space Adaptive Filter Design Sensitivity Parallel Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 1–13Google Scholar
  2. 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.Google Scholar
  3. 3.
    Pamee, I. C, (1996) Towards an Optimal Engineering Design Process Using Adaptive Search Strategies. Journal of Engineering Design 7(4). pp 341–362.Google Scholar
  4. 4.
    Jarvis R A and Patrick, E. A (1993) Clustering using a Similarity Measure Based on Shared Near Neighbours. IEEE Transactions on Computers, (22)11.Google Scholar
  5. 5.
    Parmee. I. C. (1996) Cluster-Oriented Genetic Algorithms (COGAs) for the Identification of High-Performance Regions of Design Space. Procs. EvCA Conference Moscow. June 24–27 1996Google Scholar
  6. 6.
    Reeves, C. R (1993) Using Genetic Algorithms with Small Populations. Procs. of the Fifth International Conference on Genetic Algorithms. pp 92–99Google Scholar
  7. 7.
    Brooker, L. (1987) Improving Search in Genetic Algorithms, In Davis, L. (Ed) Genetic Algorithms and Simulated Annealing pp 61–73. Morgan KaufmannGoogle Scholar
  8. 8.
    Baker, J. E., (1987) Reducing Bias and Inefficiency in the Selection Algorithm. Procs of the Second International Conference on Genetic Algorithms, pp 14–21Google Scholar
  9. 9.
    Lin, S. C. and Goodman, E. (1994) Coarse-grain parallel genetic algorithms: Categorisation new approach. Sixth IEEE symposium on Parallel and Distributed Processing, Los Alamitos, CA: IEEE Computer Society PressGoogle Scholar
  10. 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.Google Scholar
  11. 11.
    Davidor, Y., Yamada, T. & Nakano, R (1993) The ECOlogical framework II: Improving GA performance at virtually zero cost. Procs of the Fifth International Conference on genetic Algorithms. pp 171–175Google Scholar
  12. 12.
    Spiessens, P & Manderick, B. (1991) A Massively Parallel Genetic Algorithm: Implementation and First Analysis. Procs. of the Fourth International Conference on Genetic Algorithms. pp 257–263Google Scholar
  13. 13.
    Beck, M. A. (1996) Parallel Genetic Algorithms: An Investigation of Coarse and Fine Grained approaches to Design Space Decomposition. MSc Thesis. University of Plymouth.Google Scholar
  14. 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.Google Scholar
  15. 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 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • I. C. Parmee
    • 1
  • M. A. Beck
    • 1
  1. 1.Plymouth Engineering Design CentreUniversity of PlymouthDrake Circus

Personalised recommendations