AI RESEARCH
Evolving to the Aesthetics of a Vision-Language Model
arXiv CS.CV
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ArXi:2606.00112v1 Announce Type: cross Evolutionary systems have nstrated 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.