Evolving to the Aesthetics of a Vision-Language Model
Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. However, an open problem remains in designing fitness functions that effectively capture the desired aesthetics of abstract outputs. In this work, we explore two methods for evaluating the aesthetics of a population using Vision-Language Models (VLMs). The first method uses CLIP-IQA to predict an aesthetic score for each design. The second method instead pits candidates against each other, with winners determined by a VLM using a custom prompt specified by the user. The outcomes of these pairwise comparisons are then used to estimate a popula...
| Authors | Stephen James Krol, Jon McCormack |
|---|---|
| Primary category | cs.NE |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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