Exploring Automatic Fitness Evaluation for Evolutionary Typesetting

Graphic Design & Typography ToolsCreative Coding & Computational ArtComputational Methods in HCIUI/UX DesignersAI/ML Researchers & EngineersVisual Artists & Designers

The recent popularity of creative coding tools and Computational Creativity approaches are promoting a paradigm shift in the creation, development and production of Graphic Design artefacts. In this work, we present an evolutionary system for the automatic typesetting of typographic posters. This system is inspired by the letterpress typesetting process of the print houses in the earlier 19th century and employs lexicon-based approaches to recognise the semantic meaning of the posters’ content. During the evolutionary process, poster designs are automatically created and evaluated according to three objectives: legibility, aesthetics, and semantics. The system allows the users to express their preferences by specifying the intended visual features for the output designs, selecting the preferable fitness assignment strategy, and controlling different aspects of the evaluation strategy. We implemented three automatic strategies to evaluate the fitness of the posters: a multi-criteria hardwired fitness function, a multi-objective optimisation approach, and a hybrid strategy that combines features from the previous two strategies. The experimental results demonstrate the ability of the presented system to generate typographic posters, from scratch, and show the impact of the different evaluation strategies on the evolved poster designs. Overall, this research reveals how Evolutionary Computation approaches can be employed to develop novel co-creative typesetting tools and enable the automatic creation of customised typographic designs.

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https://hci.top/en/papers/cc/58350/2021

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DOI: https://doi.org/10.1145/3450741.3465247
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C&C
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Year
2021
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Authors
4 authors
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Subtopics
Graphic Design & Typography Tools, Creative Coding & Computational Art, Computational Methods in HCI
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Professions
UI/UX Designers, AI/ML Researchers & Engineers, Visual Artists & Designers
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Abstract only
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