Evolution & Foundation: AI Shares Creative Control

2026-06-15

AI Summary

This work investigates collaborative creation between human artists and artificial intelligence in generating complex 3D organic forms. It proposes an evolutionary computation framework that integrates genetic algorithms with the visual reasoning capabilities of multimodal large language models, redefining the artist’s role from direct selector to system designer while delegating fine-grained aesthetic evaluation to AI agents. The approach enables, for the first time, efficient exploration of a multidimensional evolutionary parameter space driven by AI, supported by interactive visualization and evolutionary narrative generation to provide interpretable audit trails of aesthetic decisions. Experimental results demonstrate that the system efficiently produces 3D morphologies of high aesthetic value, facilitates rapid discovery of creative solutions aligned with semantic user goals, and ensures transparency throughout the generative exploration process.

Abstract

This paper investigates the creative process of automated design and artistic evaluation using an evolutionary system. We consider how a multimodal artificial intelligence (AI) model can communicate and guide a combined generative and evolutionary computational system. This creates a framework for the evolution of aesthetically pleasing complex 3D organic forms by integrating genetic algorithms with the visual reasoning capabilities of large-scale AI foundation models. The framework shifts the artist role from that of intensive direct selection to one of system design; transferring detailed step-by-step curation to an AI agent capable of multimodal aesthetic judgement. This framework enables the human artist/designer to rapidly traverse large areas of multi-dimensional evolutionary parameter space to find creative outcomes based on their semantic targets. Detailed audit trails of the AI's aesthetic reasoning are generated for each experiment. Interactive visualisation tools, together with AI-generated summaries and evolutionary narratives, enable deep exploration into each evolutionary experiment and providing a transparent insight into the AI-guided process.

Problem

• creative control
• evolutionary design
• multimodal AI
• aesthetic evaluation
• 3D generative art

Innovation

• evolutionary computation
• foundation models
• multimodal AI
• generative design
• aesthetic reasoning