Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation
arXiv:2604.14790v2 Announce Type: replace Abstract: Interactive Evolutionary Computation (IEC) provides a powerful framework for optimizing subjective criteria such as human preferences and aesthetics, yet it suffers from a fundamental limitation: in high-dimensional generative representations, defining crossover in a semantically consistent manner is difficult, often leading to a mutation-dominated search. In this work, we explicitly define crossover in diffusion models. We propose Diffusion crossover, which formulates evolutionary recombination as step-wise interpolation of noise sequences i
The paper directly addresses a fundamental limitation in high-dimensional generative models, specifically in defining semantically consistent crossover, which has hindered more advanced evolutionary computation applications.
This breakthrough advances the capabilities of generative AI by enabling more effective evolutionary optimization, especially for subjective criteria like human preferences and aesthetics, which are critical for many AI applications.
The explicit definition of crossover in diffusion models through noise sequence interpolation introduces a new method for generating and optimizing complex outputs that better reflect human intent and creativity.
- · AI researchers
- · Generative AI companies
- · Creative industries using AI
- · Interactive Evolutionary Computation developers
- · Generative models reliant solely on mutation-dominated search
Improved generative AI models capable of more nuanced creative outputs and better alignment with subjective human preferences.
Accelerated development of AI agents that can iteratively refine designs, art, or other complex outputs through evolutionary processes.
Potential for entirely new classes of AI-generated content and tools that were previously infeasible due to limitations in controlling high-dimensional generative spaces.
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Read at arXiv cs.AI