Skip to main content

User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria

  • Chapter
Multiobjective Problem Solving from Nature

Part of the book series: Natural Computing Series ((NCS))

Summary

This chapter centres around the use of interactive evolutionary computation as a search and exploration tool for open-ended contexts in design. Such contexts are characterized by poor initial definition and uncertainty in terms of objectives, constraints and defining variable parameters. The objective of the research presented is the realization of ‘user-centric’ intelligent systems, i.e., systems which can overcome initial lack of understanding and associated uncertainty, whilst also stimulating innovation and creativity through a high degree of human / machine interaction. Two application areas are used to illustrate how, through the adoption of bespoke visualization techniques, flexible representations, and machine learning agents that ‘observe’ the evolutionary process, this objective can be achieved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abraham, J.A.R. and Parmee, I.C. (2004) Extraction of emerging multi-objective design information from COGA data, Procs. of Adaptive Computing in Design and Manufacture VI, Springer, London.

    Google Scholar 

  2. Avigad, G., Moshaiov, A. and Brauner, N. (2004) Concept-based interactive brainstorming in engineering design. J. of Advanced Computational Intelligence and Intelligent Informatics, 8(5).

    Google Scholar 

  3. Bentley, P.J. (2000) Exploring Component-Based Representations - The Secret of Creativity by Evolution? In Proc. of the Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM 2000), I. C. Parmee (ed), University of Plymouth, UK. pp. 161–172.

    Google Scholar 

  4. Bonham, C.R. and Parmee, I. C. (1999) An investigation of exploration and exploitation in cluster-oriented genetic algorithms. Procs. of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA: 1491–1497.

    Google Scholar 

  5. Carnahan, B. and Dorris, N. (2004) Identifying relevant symbol design criteria using interactive evolutionary computation, Procs. Genetic and Evolutionary Computing Conference (GECCO), Seattle, USA.

    Google Scholar 

  6. Cvetkovic, D. and Parmee, I. C. (2003) Agent-based support within an interactive evolutionary design system. J. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Cambridge University Press, 16(5): 331–342

    Article  Google Scholar 

  7. Cvetkovic, D. and Parmee, I. C. (2001) Preferences and their Application in Evolutionary Multi-objective Optimisation. IEEE Transactions on Evolutionary Computation, 6(1):42–57.

    Article  Google Scholar 

  8. Gero J. S., Louis S. J., Kundu, S. (1994) Evolutionary learning of novel grammars for design improvement. J. Artificial Intelligence for Design, Analysis and Manufacture, 8: 83–94.

    Google Scholar 

  9. Gero J. S., Shi X. G., (1999) Design development based on an analogy with developmental biology. CAADRIA ’99, J. Gu and Z. Wei (eds), Shanghai, China: 253–264.

    Chapter  Google Scholar 

  10. Gero, J.S. (2002) Computational models of creative designing based on situated cognition, in T Hewett and T Kavanagh (eds), Creativity and Cognition, New York, ACM Press, USA.

    Google Scholar 

  11. Goel, A. K. (1997) Design, analogy and creativity. IEEE Expert, Intelligent Systems and their Applications, 12(3): 62–70.

    Google Scholar 

  12. Grierson, D. E., Khajehpour, S. (2002) Method for Conceptual Design Applied to Office Buildings. Journal of Computing in Civil Engineering, 16 (2) pp. 83–103.

    Article  Google Scholar 

  13. Inselberg, A. (1985) The plane with parallel coordinates, The Visual Computer, 1: 69–91.

    Article  MathSciNet  Google Scholar 

  14. Kim, H. S., Cho, S. B. (2005) Fashion design using interactive genetic algorithm with knowledge-based encoding. In: Y. Jin (Ed), Knowledge Incorporation in Evolutionary Computation, Springer Verlag.

    Google Scholar 

  15. Machwe, A., Parmee, I. C., Miles, J. C. (2005a) Overcoming representation issues when including aesthetic criteria in evolutionary design. Procs. ASCE Int. Conf. on Computing in Civil Engineering, Cancun, Mexico.

    Google Scholar 

  16. Machwe, A., Parmee, I. C. and Miles, J. C. (2005b) Integrating Aesthetic Criteria with a user-centric evolutionary system via a component-based design representation. Proceedings of International Conference on Engineering Design, Melbourne, Australia.

    Google Scholar 

  17. Machwe, A. and Parmee, I. C. (2006a) Introducing machine learning within an interactive evolutionary design environment. Procs. Design 2006, Croatia.

    Google Scholar 

  18. Machwe, A. and Parmee, I. C. (2006b) Integrating aesthetic criteria with evolutionary processes in complex, free-form design — an initial investigation. Procs. IEEE Congress on Evolutionary Computation, Vancouver, Canada.

    Google Scholar 

  19. Parmee, I. C. (1996). The maintenance of search diversity for effective design space decomposition using cluster oriented genetic algorithms (COGAs) and multi-agent strategies (GAANT). Procs. 2nd International Conference on Adaptive Computing in Engineering Design and Control, Plymouth, UK; pp 128–138.

    Google Scholar 

  20. Parmee, I. C. (2001) Evolutionary and adaptive computing in engineering design. Springer Verlag, London.

    Book  Google Scholar 

  21. Parmee, I. C. (2002) Improving problem definition through interactive evolutionary computation. J. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 16 (3), Cambridge University Press: 185–202.

    Article  Google Scholar 

  22. Parmee, I. C. (2004) User-centric evolutionary design. Procs. Design 2004, Dubrovnic.

    Google Scholar 

  23. Parmee, I. C., Abraham, J. A. R. (2005) Interactive Evolutionary Design. In: Y. Jin (Ed), Knowledge Incorporation in Evolutionary Computation, Springer Verlag.

    Google Scholar 

  24. Parmee I. C., Abraham J. A. R. (2004) Supporting Implicit Learning via the Visualisation of COGA Multi-objective Data. Procs. IEEE Congress on Evolutionary Computation 2004, Portland: 395–402.

    Google Scholar 

  25. Parmee, I. C., Bonham, C. R. (1999) Towards the support of innovative conceptual design through interactive designer/evolutionary computing strategies. J. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Cambridge University Press 14:3–16.

    Google Scholar 

  26. Parmee, I. C., Watson, A. W. (1999) Preliminary Airframe Design using Co-evolutionary Multi-objective Genetic Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA:1651–1665.

    Google Scholar 

  27. Parmee, I., Watson, A., Cvetkovic, D., Bonham, C. R. (2000) Multi-objective Satisfaction within an Interactive Evolutionary Design Environment. Journal of Evolutionary Computation; MIT Press; 8(2): 197–222.

    Article  Google Scholar 

  28. Rosenman, M. A. (1997) An exploration into evolutionary models fornon-routine design, Artificial Intelligence in Engineering, 11(3):287–293.

    Article  Google Scholar 

  29. Su N. P. (1990) The principles of design. Oxford University Press, New York.

    Google Scholar 

  30. Takagi, H., Ohsaki, M. (1999) IEC-based Hearing Aid Fitting. Procs. International Conference on System, Man and Cybernetics (SMC ’99) ,IEEE, Vol 3, 657–662.

    Google Scholar 

  31. Zitzler, E., Laumanns, M., Thiele, L. (2002) SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. Procs. Evolutionary Methods for Design, Optimisation, and Control, CIMNE, Barcelona: 95–100.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Parmee, I.C., Abraham, J.A.R., Machwe, A. (2008). User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72964-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72963-1

  • Online ISBN: 978-3-540-72964-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics