Residual-Space Evolutionary Optimization via Flow-based Generative Models

• URL: http://arxiv.org/abs/2606.20084v1
• Date: Thu, 18 Jun 2026 11:02:11 GMT
• Title: Residual-Space Evolutionary Optimization via Flow-based Generative Models
• Abstract summary: We introduce residual-space evolutionary optimization, a model-agnostic framework that combines flow-based generative editing with evolutionary algorithms.<n>As a proof of concept, we validate on MorphoMNIST, a benchmark dataset for counterfactual generation.
• Score: 42.567799767855035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these assumptions break down in flow-based settings, where edits are performed through forward and backward integration and often involve non-differentiable or black-box objectives. We introduce residual-space evolutionary optimization, a model-agnostic framework that addresses this gap by combining flow-based generative editing with evolutionary algorithms. Building on the observation that conditional flow matching (CFM) can disentangle condition-controlled factors from instance-specific residuals, our framework directly operates in residual space and separates two complementary search regimes: self-pollination performs local exploitation through feature-preserving residual refinement, and cross-pollination promotes broader exploration by recombining residuals across heterogeneous samples. As a proof of concept, we validate on MorphoMNIST, a benchmark dataset for counterfactual generation, and on crystal data, demonstrating that this exploration--exploitation decomposition provides a useful mechanism for balancing target alignment, instance preservation, and diversity, and extends beyond images to real-world scientific domains.

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