Aesthetic Design Using Multi-Objective Evolutionary Algorithms
The use of computational methodologies for the optimization of aesthetic parameters is not frequent mainly due to the fact that these parameters are not quantifiable and are subjective. In this work an interactive methodology based on the use of multi-objective optimization algorithms is proposed. This strategy associates the results of different optimization runs considering the existent quantifiable objectives and different sets of boundary conditions concerning the decision variables, as defined by an expert decision maker. The associated results will serve as initial population of solutions for a final optimization run. The idea is that a more global picture of potential “good” solutions can be found. At the end this will facilitate the work of the expert decision maker since more solutions are available. The method was applied to a case study and the preliminary results obtained showed the potentially of the strategy adopted.
Keywordsaesthetic design multi-objective evolutionary algorithms
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- 1.Reffat, R.M.: Computing in architectural design: reflections and an approach to new generations of CAAD. The Journal of Information Technology in Construction 11, 655–668 (2006)Google Scholar
- 3.Kolarevic, B.: Architecture in the Digital Age: Design and Manufacturing. Spon Press, New York (2003)Google Scholar
- 5.Caldas, L.G.: An evolution-based generative design system : using adaptation to shape architectural form. PhD Thesis, Massachusetts Institute of Technology (August 23, 2005)Google Scholar
- 7.Kilian, A.: Design exploration through bidirectional modelling of constraints. PhD Thesis, Massachusetts Institute of Technology (August 25, 2006)Google Scholar
- 8.Kolarevic, B., Malkawi, A.M.: Performative Architecture: Beyond Instrumentality. Spon Press, London (2005)Google Scholar
- 9.van Hinte, E., Beukers, A.: Lightness: The Inevitable Renaissance of Minimum Energy Structures, 3rd edn., Uitgeverij, Rotterdam (1998)Google Scholar
- 11.Rogers, D.F.: An Introduction to NURBS with Historical Perspective. Morgan Kaufmann, London (2001)Google Scholar
- 12.Robert McNeel & Associates, www.rhino3d.com (accessed in 2010)
- 13.Autodesk - Ecotect, www.autodesk.com/ecotect (accessed in 2010)
- 17.Ferreira, J.C., Fonseca, C.M., Gaspar-Cunha, A.: Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization. In: IEEE Congress on Evolutionary Computation (IEEE CEC 2009), Trondheim, Norway (2009)Google Scholar
- 18.Gaspar-Cunha, A.: Modeling and Optimization of Single Screw Extrusion. PhD Thesis, University of Minho, Portugal (2000)Google Scholar
- 19.Gaspar-Cunha, A., Covas, J.A.: RPSGAe-A Multiobjective Genetic Algorithm with Elitism: Application to Polymer Extrusion. In: Metaheuristics for Multiobjective Optimisation. Lecture Notes in Economics and Mathematical Systems. Springer, Heidelberg (2004)Google Scholar