EvoDesigner: Towards aiding creativity in graphic design

In Graphic Design (GD), finding disruptive solutions that attract people’s attention is of the utmost importance. However, to deliver faster and cheaper, often designers adopt trendy solutions rather than exploring innovative ones.
EvoDesigner (see Figure 2) is an automatic system to assist the creative process of graphic designers by alternately collaborating with these in the evolution/edition of pages and page items within the Adobe inDesign environment, e.g. for creating posters.

 

Figure 1

Examples of posters generated using: various setups, regarding various generations and slightly different versions of the system.

 

Figure 2


Schematic representation of EvoDesigner.

 

To use the system, the user must 1) create a blank document, 2) insert elements into pages (see Figure 4), 3) set up desired preferences (e.g. set the pages to evolve and set keywords) and click “Generate” to start; 4) the module Keywords-to-visuals translation will try to find properties/tools that match the inserted keywords; 5) Each property/tool will be assigned with a probability to be used by the system to mutate pages (individuals); 6) the evolutionary engine will evolve pages 7) the resulting pages will be made available as normal and editable inDesign pages; lastly, 8) the designer may edit the results and 9) export final artefact. From any stage of the user interaction, the parameters might be changed and the evolution restarted. See Figure 3 for a scheme of the genotype.

 

Figure 3

Schematic representation of an individual’s genotype, (property names and value-types might not be fully accurate).

 

This work presents the developments on the first iteration of EvoDesigner, consisting of the implementation of an automatic evolutionary engine based on a conventional genetic algorithm.
  Experiments have been done for evolving page layouts towards given target images, using the Mean Squared Error (MSE) metric for assessing fitness. Figure 4 presents the 3 manually created phenotypes evolved. Figure 5 showcases 10 initial posters, automatically generated from the posters in Figure 4.
 

Figure 4

Evolved manually created pages.

 

Figure 5

Example of an initial population of 10 individuals, generated out of the 3 selected pages of Figure 4.

 

The presented experiments mainly focused on targeting sketches of posters (see Figure 6). Sketched targets might be useful, for example, whenever a graphic designer aims to generate artefacts that describe a given page balance and colour pallet. Nevertheless, utilising images of finished gd artefacts might also be useful, for example, for resembling the page balance of the targets without culminating in results that are too similar to the originals.
For instance, the generated artefacts might include page items that are completely different from the ones in the target posters.
 

Figure 6

Best individuals from 4 different runs (100 generations), for 3 different target images: a) Figure 5.a.1; b) Figure 5.a.2; c) Figure 5.a.3

 

The performed experiments suggested the viability of the presented approach in the evolution of gd artefacts that resemble the page balance of the target images, but which are different enough not to be deemed as the same. Besides user testing must be needed, we believe the presented approach might be worth being included in the gd workflow for assisting the generation of new gd solutions since the system is able to take given layouts and consider these to dispose and edit page items in relatively unexpected manners
 

In future work, several different modules for improving the robustness of the system must be developed, such as (i) a module for translating keywords into visual properties/tools (e.g. for limiting the search space towards a given creative concept), or (ii) fitness modules that can or not be used for performing novelty, legibility and balance judgements, or assessing how much an image might be in-style with a given gd aesthetic movement. Also, new functionalities must be added, e.g. for positioning items according to page grids, promoting more organised layouts.
 

Figure 1 showcases examples of posters generated using different versions of the system, which must be further tested in future developments.

 

Publications

 

  • D. Lopes, J. Correia, and P. Machado, “EvoDesigner: Towards Aiding Creativity in Graphic Design,” in Artificial Intelligence in Music, Sound, Art and Design – 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings, 2022, pp. 162-178.