Performance-Driven Generative Design in Buildings: A Systematic Review

Buildings are under increasing pressure to address decarbonization and climate adaptation, which is pushing design practice from post hoc performance checks to performance-driven generative design (PDGD). This review maps the current state of PDGD in buildings and proposes an engineering-oriented framework that links research methods to deployable workflows. Using a PRISMA-based systematic search, we identify 153 core studies and code them along five dimensions: design objects and scales, objectives and metrics, algorithms and tools, workflows, and data and validation. The corpus shows a strong focus on facades, envelopes, and single-building massing, dominated by energy, daylight and thermal comfort objectives, and a widespread reliance on parametric platforms connected to performance simulation software with multi-objective optimization. From this evidence we extract three typical workflow routes: parametric evolutionary multi-objective optimization, surrogate or Bayesian optimization, and data- or model-driven generation. Persistent weaknesses include fragmented metric conventions, limited cross-case or field validation, and risks to reproducibility. In response, we propose a harmonized objective–metric system, an evidence pyramid for PDGD, and a reproducibility checklist with practical guidance, which together aim to make PDGD workflows more comparable, auditable, and transferable for design practice.