The paper presents a generative design approach, particularly for simulation-driven designs, using a genetic algorithm (GA), which is structured based on a novel offspring selection strategy. The proposed selection approach commences while enumerating the offsprings generated from the selected parents. Afterwards, a set of eminent offsprings is selected from the enumerated ones based on the following merit criteria: space-fillingness to generate as many distinct offsprings as possible, resemblance/non-resemblance of offsprings to the good/bad individuals, non-collapsingness to produce diverse simulation results and constrain-handling for the selection of offsprings satisfying design constraints. The selection problem itself is formulated as a multi-objective optimization problem. A greedy technique is employed based on non-dominated sorting, pruning, and selecting the representative solution. According to the experiments performed using three different application scenarios, namely simulation-driven product design, mechanical design and user-centred product design, the proposed selection technique outperforms the baseline GA selection techniques, such as tournament and ranking selections.
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Abd-El-Wahed W, Mousa A, El-Shorbagy M (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J Comput Appl Math 235(5):1446–1453
Affenzeller M, Wagner S (2005) Offspring selection: a new self-adaptive selection scheme for genetic algorithms. In: Adaptive and natural computing algorithms. Springer, pp 218–221
Al Jadaan O, Rajamani L, Rao C (2008) Improved selection operator for GA. J Theor Appl Inf Technol 4(4):269–277
Anand S, Afreen N, Yazdani S (2015) A novel and efficient selection method in genetic algorithm. Int J Comput Appl 129(15):7–12
Ang MC, Chau HH, Mckay A, Pennington AD (2006) Combining evolutionary algorithms and shape grammars to generate branded product design. In: Design computing and cognition. Springer, pp 521–539
Audze P, Eglais V (1977) New approach for planning out of experiments. Probl Dyn Strengths 35:104–107
Blickle T, Thiele L (1996) A comparison of selection schemes used in evolutionary algorithms. Evol Comput 4(4):361–394
Cai J, Thierauf G (1993) Discrete optimization of structures using an improved penalty function method. Decis Control 21(4):293–306
Caldas L (2008) Generation of energy-efficient architecture solutions applying gene\_arch: an evolution-based generative design system. Adv Eng Inf 22(1):59–70
Chase SC (2005) Generative design tools for novice designers: issues for selection. Autom Constr 14(6):689–698
Cheikh M, Jarboui B, Loukil T, Siarry P (2010) A method for selecting pareto optimal solutions in multiobjective optimization. J Inf Math Sci 2(1):51
Chen B, Pan Y, Wang J, Fu Z, Zeng Z, Zhou Y, Zhang Y (2013) Even sampling designs generation by efficient spatial simulated annealing. Math Comput Model 58(3–4):670–676
Cluzel F, Yannou B, Dihlmann M (2012) Using evolutionary design to interactively sketch car silhouettes and stimulate designer’s creativity. Eng Appl Artif Intel 25(7):1413–1424
Cui J, Tang MX (2013) Integrating shape grammars into a generative system for zhuang ethnic embroidery design exploration. Comput Aided Des 45(3):591–604
Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl math comput 188(1):895–911
Dogan KM, Suzuki H, Gunpinar E, Kim MS (2019) A generative sampling system for profile designs with shape constraints and user evaluation. Comput Aided Des 111:93–112
Dorst K, Cross N (2001) Creativity in the design process: co-evolution of problem-solution. Des Stud 22(5):425–437
Elfeky EZ, Sarker RA, Essam DL (2008) Analyzing the simple ranking and selection process for constrained evolutionary optimization. J Comput Sci Technol 23(1):19–34
Fisher M, Ritchie D, Savva M, Funkhouser T, Hanrahan P (2012) Example-based synthesis of 3d object arrangements. ACM Trans Graph 31(6):135
Fuerle F, Sienz J (2011) Formulation of the audze-eglais uniform latin hypercube design of experiments for constrained design spaces. Adv Eng Softw 42(9):680–689
Gen M, Cheng R (2007) Genetic algorithms and engineering optimization. Wiley, London
Goh KS, Lim A, Rodrigues B (2003) Sexual selection for genetic algorithms. Artif Intel Rev 19(2):123–152
Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms, vol 1. Elsevier, Amsterdam, pp 69–93
Granadeiro V, Pina L, Duarte JP, Correia JR, Leal VM (2013) A general indirect representation for optimization of generative design systems by genetic algorithms: application to a shape grammar-based design system. Autom Constr 35:374–382
Guide WSC (2018) Sprint car chassis. http://www.world-sprintcar-guide.com/
Gunpinar E, Gunpinar S (2018) A shape sampling technique via particle tracing for CAD models. Graph Models 96:11–29
Gunpinar E, Coskun UC, Ozsipahi M, Gunpinar S (2019) A generative design and drag coefficient prediction system for sedan car side silhouettes based on computational fluid dynamics. Comput Aided Des 111:65–79
Jafari-Marandi R, Smith BK (2017) Fluid genetic algorithm (FGA). J Comput Des Eng 4(2):158–167
Julstrom BA (1999) It’s all the same to me: revisiting rank-based probabilities and tournaments. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 2. IEEE, pp 1501–1505
Kalogerakis E, Chaudhuri S, Koller D, Koltun V (2012) A probabilistic model for component-based shape synthesis. ACM Trans Graph 31(4):55
Kazi RH, Grossman T, Cheong H, Hashemi A, Fitzmaurice G (2017) Dreamsketch: Early stage 3d design explorations with sketching and generative design. In: Proceedings of the 30th annual ACM symposium on user interface software and technology. ACM, pp 401–414
Kelly G, McCabe H (2006) Interactive generation of cities for real-time applications. In: ACM SIGGRAPH 2006 research posters. ACM, p 44
Khan S, Awan MJ (2018) A generative design technique for exploring shape variations. Adv Eng Inf 38:712–724
Khan S, Gunpinar E (2018) Sampling cad models via an extended teaching-learning-based optimization technique. Comput Aided Des 100:52–67
Khan S, Gunpinar E, Moriguchi M (2017) Customer-centered design sampling for cad products using spatial simulated annealing. In: Proceedings of CAD’17, Okayama, Japan, pp 100–103
Kitchley JJL, Srivathsan A (2014) Generative methods and the design process: a design tool for conceptual settlement planning. Appl Soft Comput 14:634–652
Krish S (2011) A practical generative design method. Comput Aided Des 43(1):88–100
Mashohor S, Evans JR, Arslan T (2005) Elitist selection schemes for genetic algorithm based printed circuit board inspection system. In: The 2005 IEEE congress on evolutionary computation, vol 2. IEEE, pp 974–978
McCormack JP, Cagan J (2002) Designing inner hood panels through a shape grammar based framework. Ai Edam 16(4):273–290
Ono I, Kita H, Kobayashi S (2003) A real-coded genetic algorithm using the unimodal normal distribution crossover. In: Ghosh A, Tsutsui S (eds) Advances in evolutionary computing. Natural Computing Series. Springer, Berlin, Heidelberg, pp 213–237
Palubicki W, Horel K, Longay S, Runions A, Lane B, Měch R, Prusinkiewicz P (2009) Self-organizing tree models for image synthesis. ACM Trans Graph 28(3):58
Prusinkiewicz P, Shirmohammadi M, Samavati F (2012) L-systems in geometric modeling. Int J Found Comput Sci 23(01):133–146
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Runions A, Fuhrer M, Lane B, Federl P, Rolland-Lagan AG, Prusinkiewicz P (2005) Modeling and visualization of leaf venation patterns. ACM Trans Graph 24(3):702–711
Shea K, Aish R, Gourtovaia M (2005) Towards integrated performance-driven generative design tools. Autom Constr 14(2):253–264
Singh V, Gu N (2012) Towards an integrated generative design framework. Des Stud 33(2):185–207
Sousa JP, Xavier JP (2015) Symmetry-based generative design and fabrication: a teaching experiment. Autom Constr 51:113–123
Stiny G (1980) Introduction to shape and shape grammars. Environ Plan B Plan Des 7(3):343–351
Subasi A, Sahin B, Kaymaz I (2016) Multi-objective optimization of a honeycomb heat sink using response surface method. Int J Heat Mass Transfer 101:295–302
Subbaraj P, Rengaraj R, Salivahanan S (2011) Enhancement of self-adaptive real-coded genetic algorithm using taguchi method for economic dispatch problem. Appl Soft Comput 11(1):83–92
Sudeng S, Wattanapongsakorn N (2015) Post pareto-optimal pruning algorithm for multiple objective optimization using specific extended angle dominance. Eng Appl Artif Intel 38:221–236
Turrin M, von Buelow P, Stouffs R (2011) Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Adv Eng Inf 25(4):656–675
Usta VM, Onder GM (2017) Dental implant design for mandibular first molar tooth and material optimization with finite element analysis. Bachelor thesis, Istanbul Technical University
Vaissier B, Pernot JP, Chougrani L, Véron P (2019) Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing. Comput Aided Des 110:11–23
Yu W, Li B, Jia H, Zhang M, Wang D (2015) Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build 88:135–143
Zhong J, Hu X, Zhang J, Gu M (2005) Comparison of performance between different selection strategies on simple genetic algorithms. In: international conference on intelligent agents, web technologies and internet commerce, international conference on computational intelligence for modelling, control and automation, vol 2. IEEE, pp 1115–1121
The authors would like to thank The Scientific and Technological Research Council of Turkey for supporting this research (Project No. 315M077), and Veysel Mert Usta and Gani Melik Onder to perform FEM tests for the dental implant models.
Conflict of interest
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author.
This study was funded by The Scientific and Technological Research Council of Turkey (Project No. 315M077).
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Gunpinar, E., Khan, S. A multi-criteria based selection method using non-dominated sorting for genetic algorithm based design. Optim Eng 21, 1319–1357 (2020). https://doi.org/10.1007/s11081-019-09477-8
- Computer-aided design
- Genetic algorithm
- Mating selection
- Multi-objective optimization