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Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9018))

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Abstract

Multi-objective optimization yields multiple solutions each of which is no better or worse than the others when the objectives are conflicting. These solutions lie on the Pareto-optimal front which is a lower-dimensional slice of the objective space. Together, the solutions may possess special properties that make them optimal over other feasible solutions. Innovization is the process of extracting such special properties (or design principles) from a trade-off dataset in the form of mathematical relationships between the variables and objective functions. In this paper, we deal with a closely related concept called temporal innovization. While innovization concerns the design principles obtained from the trade-off front, temporal innovization refers to the evolution of these design principles during the optimization process. Our study indicates that not only do different design principles evolve at different rates, but that they start evolving at different times. We illustrate temporal innovization using several examples.

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References

  1. Bandaru, S., Deb, K.: Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique. Engineering Optimization 43(9), 911–941 (2011)

    Article  Google Scholar 

  2. Bendsøe, M.: Optimal shape design as a material distribution problem. Structural and Multidisciplinary Optimization 1(4), 193–202 (1989)

    Article  Google Scholar 

  3. Datta, D., Deb, K.: Design of optimum cross-sections for load-carrying members using multi-objective evolutionary algorithms. In: Proceedings of International Conference on Systemics, Cybernetics and Informatics, pp. 571–577 (2005)

    Google Scholar 

  4. Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  5. Deb, K., Bandaru, S., Tutum, C.C.: Temporal evolution of design principles in engineering systems: Analogies with human evolution. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 1–10. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. Deb, K., Gupta, S., Daum, D., Branke, J., Mall, A., Padmanabhan, D.: Reliability-based optimization using evolutionary algorithms. IEEE Trans. on Evolutionary Computation 13(5), 1054–1074 (2009)

    Article  Google Scholar 

  7. Deb, K., Srinivasan, A.: Innovization: Innovating design principles through optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 1629–1634. ACM, New York (2006)

    Google Scholar 

  8. Deb, K., Bandaru, S., Greiner, D., Gaspar-Cunha, A., Tutum, C.C.: An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Applied Soft Computing 15, 42–56 (2014)

    Article  Google Scholar 

  9. Fedder, G., Iyer, S., Mukherjee, T.: Automated optimal synthesis of microresonators. In: Proceedings of the Ninth Int. Conf. Solid State Sens. Actuators, Chicago, IL, pp. 1109–1112, April 1997

    Google Scholar 

  10. Fedder, G., Mukherjee, T.: Physical design for surface-micromachined MEMS. In: Proceedings of the Fifth ACM SIGDA Physical Design Workshop, Virginia, USA, April 1996

    Google Scholar 

  11. Haeckel, E.: The evolution of man, vol. 1. Kessinger Publishing (1879)

    Google Scholar 

  12. Kreimer, G.: The green algal eyespot apparatus: A primordial visual system and more? Current Genetics 55(1), 19–43 (2009)

    Article  Google Scholar 

  13. Land, M., Fernald, R.: The evolution of eyes. Annual Review of Neuroscience 15(1), 1–29 (1992)

    Article  Google Scholar 

  14. Quiza Sardiñas, R., Rivas Santana, M., Alfonso Brindis, E.: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence 19(2), 127–133 (2006)

    Article  Google Scholar 

  15. Rozvany, G.: Aims, scope, methods, history and unified terminology of computer-aided topology optimization in structural mechanics. Structural and Multidisciplinary Optimization 21(2), 90–108 (2001)

    Article  Google Scholar 

  16. Rozvany, G.: A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization 37(3), 217–237 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  17. Sigmund, O.: A 99 line topology optimization code written in matlab. Structural and Multidisciplinary Optimization 21(2), 120–127 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. Virtual Systems Research Centre, University of Skövde, 541 28, Skövde, Sweden

    Sunith Bandaru

  2. Department of Electrical and Computer Engineering, Michigan State University, 428 S. Shaw Lane, 2120 EB, East Lansing, MI, 48824, USA

    Kalyanmoy Deb

Authors
  1. Sunith Bandaru
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  2. Kalyanmoy Deb
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Corresponding author

Correspondence to Sunith Bandaru .

Editor information

Editors and Affiliations

  1. Institute for Polymers and Composites/I3N,University of Minho, Guimaraes, Portugal

    António Gaspar-Cunha

  2. Dept. of Electrical and Computer Engg, University of Coimbra, Coimbra, Portugal

    Carlos Henggeler Antunes

  3. CINVESTAV-IPN Depto. de Computacíon, Col. San Pedro Zacatenco, México DF, Mexico

    Carlos Coello Coello

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Bandaru, S., Deb, K. (2015). Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-15934-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15933-1

  • Online ISBN: 978-3-319-15934-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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