Agent-based Support within an Interactive Evolutionary Design System

  • Dragan Cvetkovic
  • Ian Parmee


The paper concerns the integration of software agents within an interactive evolutionary design system. Different agent classes are introduced and their function within the system is explained. A preference modification agent is developed and an example is given illustrating the use of agents in preference modelling.


Multiobjective Optimisation Interface Agent Wing Span Adaptive Computing Conceptual Design Process 
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  1. 1.
    Parmee I. C, Bonham C. R., 1999, Towards the Support of Innovative Conceptual Design Through Interactive Designer / Evolutionary Computing Strategies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal; Cambridge University Press, 14, pp 3–16.Google Scholar
  2. 2.
    D’Ambrosio J. G., Birmingham W. P., 1995, Preference-directed Design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal, 9, pp 219–230.CrossRefGoogle Scholar
  3. 3.
    Wellman M. P., Walsh W. E., 2000, Distribute Quiescence Detection in Multiagent Detection. Procs. Fourth International Conference on Multiagent Systems, Boston, USA; pp 317–324.Google Scholar
  4. 4.
    Parmee I. C, Cvetkovic D, Watson A. H., Bonham C. R., 2000, Multi-objective Satisfaction within an Interactive Evolutionary Design Environment. Evolutionary Computation. 8(2), pp 197–222.CrossRefGoogle Scholar
  5. 5.
    Parmee I. C, Cvetkovic D., A. H., Bonham C. R., Packham I, 2001, Introducing Prototype Interactive Evolutionary Systems for Ill-defined Design Environments. Journal of Advances in Engineering Software, 32(6), Elsevier, pp 429–441.MATHCrossRefGoogle Scholar
  6. 6.
    Parmee I. C, 1996a, The Maintenance of Search Diversity for Effective Design Space Decomposition using Cluster-Orientated Genetic Algorithms (COGAs) and Multi-Agent Strategies (GAANT). Proceedings of 2nd International Conference on Adaptive Computing in Engineering Design and Control, PEDC, University of Plymouth; pp 128–138.Google Scholar
  7. 7.
    Bonham C. R., Parmee I. C, 1999b, Improving the Performance of Clusteroriented Genetic Algorithms (COGAs). Proceedings of IEEE Congress on Evolutionary Computation, Washington D.C., pp 554–561.Google Scholar
  8. 8.
    Cvetkovic D., Parmee I. C, 2000, Designer’s Preferences and Multi-objective Preliminary Design Processes. Evolutionary Design and Manufacture; Proceedings of the Fourth International Conference on Adaptive Computing in Design and Manufacture. Springer; pp 249–260.Google Scholar
  9. 9.
    Cvetkovic D., 2000, Evolutionary Multi-objective Decision Support Systems for Conceptual Design. PhD Thesis, University of PlymouthGoogle Scholar
  10. 10.
    Fodor J., Roubens M., 1994, Fuzzy Preference Modelling and Multi-criteria Decision Support. System Theory, Knowledge Engineering and Problem Solving, 14; Kluwer Academic PublishersGoogle Scholar
  11. 11.
    Parmee I. C, Watson A. W., 1999, Preliminary Airframe Design using Coevolutionary Multi-objective Genetic Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, Florida, USA; 1657–1665.Google Scholar
  12. 12.
    Wellman M. P., Doyle J., 1991, Preferential Semantics for Goals. Procs, Ninth International Conference on Artificial Intelligence, Vol 2, AAAI Press/MIT Press, pp 698–703.Google Scholar
  13. 13.
    Cvetkovic D., Parmee I. C, 2001, Preferences and their Application in Evolutionary Multiobjective Optimisation.” IEEE Transactions on Evolutionary Computation (in press).Google Scholar
  14. 14.
    Wooldridge M. J., Jennings N. R., 1995, Intelligent Agents: Theory and Practice. Knowledge Engineering Review, 10(2), pp 115–152.CrossRefGoogle Scholar
  15. 15.
    Stenmark D., 1999, Evaluation of Intelligent Software Agents. Web page:
  16. 16.
    Brown D. C, Dunskus B. V. et al., 1995, SINE; Support for single Function Agents. Procs Applications of AI in Engineering, AIENG ‘95 Udine, Italy.Google Scholar
  17. 17.
    Webb, E. 1997. MINICAPS — a simplified version of CAPS for use as a research tool. Unclassified Report Bae-WOA-RP-GEN-11313, British Aerospace Pic.Google Scholar
  18. 18.
    Cvetkovic D., Parmee I. C, 1999, Genetic Algorithm Based Multi-objective Optimisation and Conceptual Engineering Design. Procs IEEE Congress on Evolutionary Computation, CEC′99, Washington DC, USA, pp 29–36Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  1. 1.Soliton Associates Ltd.Toronto ONCanada
  2. 2.University of the West of EnglandBristol, BSUK

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