Abstract
The number of designers and artists who use code is growing every day. One key advantage of the computational abstraction of ideas lies in defining rules and constraints to generate numerous artefacts from a single visual concept. In this context, the variability among the artefacts is often achieved in an exploratory step of manual parametric manipulations that affect the output. However, due to the number of parameters and the resulting combinatorial explosion, most designers are limited to exploring only portions of the design space. In this paper, we address this limitation by presenting the tool EvoProteus, which takes a Processing program as input and, using a Genetic Algorithm (GA), evolves parametric variations of that program. We also experiment with three methods to evaluate the visual outcomes produced by the evolved Processing programs: a human-guided, a prompt-based using CLIP and a hybrid strategy that combines the first two methods. In line with an emergent landscape of Artificial Intelligence (AI) applications to visual domains, we study the ability of the proposed tool to enhance the exploration of parameters in Generative Design and promote human-computer co-creation.
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Acknowledgments
This work is financed through national funds by FCT - Foundation for Science and Technology, I.P., in the framework of Project UIDB/00326/2020 and UIDP/00326/2020. This work was partially funded by project No. 7059 - Neuraspace - AI fights Space Debris, reference C644877546-00000020, supported by the RRP - Recovery and Resilience Plan and the European Next Generation EU Funds, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation.
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Sacadura, R., Gonçalo, L., Martins, T., Machado, P. (2025). Expanding Design Horizons: Evolutionary Tool for Parametric Design Exploration with Interactive and CLIP-Based Evaluation. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_7
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