Open Access
Issue |
SHS Web Conf.
Volume 198, 2024
EduBIM2024 : Données, intelligences et nature de la ville durable
|
|
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Article Number | 02002 | |
Number of page(s) | 15 | |
Section | Conception assistée par les données | |
DOI | https://doi.org/10.1051/shsconf/202419802002 | |
Published online | 11 October 2024 |
- I. Caetano, L. Santos, and A. Leitão (2020), Computational design in architecture: Defining parametric, generative, and algorithmic design, Frontiers of Architectural Research, 9 (2), pp. 287–300. [CrossRef] [Google Scholar]
- J.E. Harding and P. Shepherd (2017), Meta-Parametric Design, Design Studies, 52, pp. 73–95. [CrossRef] [Google Scholar]
- L. Ma (2015), Invention architecturale et algorithmes non-lineaires, https://www.theses.fr/2015VERS023S. [Google Scholar]
- S. Zhao and E. de Angelis (2018), Performance-based Generative Architecture Design: A Review on Design Problem Formulation and Software Utilization, Journal of Integrated Design and Process Science. 22(3), pp. 55–76. [Google Scholar]
- W. Ma, X. Wang, J. Wang, X. Xiang, and J. Sun (2021), Generative Design in Building Information Modelling (BIM): Approaches and Requirements, Sensors. 21(16), pp. 5439. [Google Scholar]
- Generative Design Primer, https://www.generativedesign.org. [Google Scholar]
- X. Shen, A. Singhvi, A. Mengual, M. Spastri, and V. Watson (2018), Evaluating the Multi-Objective Optimization Methodology for Performance-Based Building Design in Professional Practice, ASHRAE and IBPSA, pp. 646–653. [Google Scholar]
- M. Bernal, V. Okhoya, T. Marshall, C. Chen, and J. Haymaker (2020), Integrating expertise and parametric analysis for a data-driven decision-making practice, International Journal of Architectural Computing, 18 (4), pp. 424–440. CUMINCAD. [CrossRef] [Google Scholar]
- S. Li, L. Liu, and C. Peng (2020), A review of performance-oriented architectural design and optimization in the context of sustainability: Dividends and challenges, Sustainability, 12 (4), pp. 1427. [CrossRef] [Google Scholar]
- C. Duclos-Prévet, F. Guéna, and M. Efron (2022), Constraint handling methods for a generative envelope design using genetic algorithms: The case of a highly constrained problem, International Journal of Architectural Computing, 20 (3), pp. 587–609. [CrossRef] [Google Scholar]
- C.A. Coello Coello (2002), Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191 (11), pp. 1245–1287. [CrossRef] [Google Scholar]
- Z. Michalewicz and M. Schoenauer (1996), Evolutionary algorithms for constrained parameter optimization problems, Evolutionary computation. 4 (1), pp. 1–32. [Google Scholar]
- C.A. Coello Coello (2016), Constraint-handling techniques used with evolutionary algorithms. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 563–587. [CrossRef] [Google Scholar]
- C. Duclos-Prévet, F. Guéna, and M. Efron (2021), Constrained Multi-Criteria Optimization for Integrated Design in Professional Practice. Gomez, P. and Braida, F. (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8-12 November 2021, pp. 29–40. CUMINCAD. [Google Scholar]
- S. Salcedo-Sanz (2009), A survey of repair methods used as constraint handling techniques in evolutionary algorithms, Computer science review, 3 (3), pp. 175–192. [CrossRef] [Google Scholar]
- A. Smith, D. Coit, T. Bäck, D. Fogel, and Z. Michalewicz (1998), Penalty Functions. [Google Scholar]
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan (2002), A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6 (2), pp. 182–197. [CrossRef] [Google Scholar]
- D. Orvosh and L. Davis (1993), Shall We Repair? Genetic Algorithms Combinatorial Optimization and Feasibility Constraints, Presented at the Proceedings of the 5th International Conference on Genetic Algorithms. [Google Scholar]
- G. Stiny: Shape Rules (1994), Closure, Continuity, and Emergence,, Environ Plann B Plann Des, 21 (7), pp. S49–S78. [CrossRef] [Google Scholar]
- V. Singh and N. Gu (2012), Towards an integrated generative design framework. Design Studies, 33 (2), pp. 185–207. [CrossRef] [Google Scholar]
- J. Ferber (1997), Les systèmes multi-agents: un aperçu général, Techniques et sciences informatiques, 16(8). [Google Scholar]
- J. Von Neumann and A.W. Burks (1966), Theory of self-reproducing automata IEEE Transactions on Neural Networks, 5 (1), pp. 3–14. [Google Scholar]
- G. Beni (2004), From swarm intelligence to swarm robotics, International Workshop on Swarm Robotics, pp. 1–9. Springer. [Google Scholar]
- R. Hu, Z. Huang, Y. Tang, O. Van Kaick, H. Zhang, and H. Huang (2020), “Graph2Plan: learning floorplan generation from layout graphs”, ACM Trans. Graph. 39(4). [Google Scholar]
- H. Zheng and P.F. Yuan (2021), A generative architectural and urban design method through artificial neural networks, Building and Environment, 205, pp. 108–178. [Google Scholar]
- C.M. Macal (2016), Everything you need to know about agent-based modelling and simulation Journal of Simulation, 10 (2), pp. 144–156. [CrossRef] [Google Scholar]
- C.M. Macal and M.J. North (2005), Tutorial on agent-based modeling and simulation, Proceedings of the Winter Simulation Conference, p. 14. [Google Scholar]
- M.S. Roudsari, M. Pak, and A. Smith (2013), Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design, Proceedings of the 13th international IBPSA conference held in Lyon, France Aug. pp. 3128–3135. [Google Scholar]
- S.B. Parascho (2013), Design Tools for Integrative Planning Stouffs, Rudi and Sariyildiz, Sevil (eds.), Computation and Performance - Proceedings of the 31st eCAADe Conference, 2, Faculty of Architecture, Delft University of Technology, Delft, The Netherlands, 18-20 September 2013, pp. 237–246. CUMINCAD. [Google Scholar]
- P.J.R. and R.K. Veloso (2019), Multi-agent space planning: a literature review (2008-2017). Ji-Hyun Lee (Eds.) “Hello, Culture!” [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 52–74. CUMINCAD. [Google Scholar]
- R. Koenig and G. Schmitt (2016), Backcasting and a New Way of Command in Computational Design, presented at the CAADence in Architecture. [Google Scholar]
- J. McCormack, A. Dorin, and T. Innocent (2004), Generative Design: A Paradigm for Design Research, DRS Biennial Conference Series. [Google Scholar]
- C. Duclos-Prévet, F. Guéna, and M. Efron (2022), Algorithme génétique ou automate cellulaire : le cas d’une optimisation multicritère sous contraintes pour la conception d’une enveloppe, SHS Web of Conferences. EDP Sciences. [Google Scholar]
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