GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture

Abstract

AbstractGENE_ARCH is an evolution-based Generative Design System that uses adaptation to shape energy-efficient and sustainable architectural solutions. The system applies goal-oriented design, combining a Genetic Algorithm (GA) as the search engine, with DOE2.1E building simulation software as the evaluation module. The GA can work either as a standard GA or as a Pareto GA, for multicriteria optimization. In order to provide a full view of the capacities of the software, different applications are discussed: 1) Standard GA: testing of the software; 2) Standard GA: incorporation of architecture design intentions, using a building by architect Alvaro Siza; 3) Pareto GA: choice of construction materials, considering cost, building energy use, and embodied energy; 4) Pareto GA: application to Siza’s building; 5) Standard GA: Shape generation with single objective function; 6) Pareto GA: shape generation with multicriteria; 7) Pareto GA: application to an urban and housing context. Overall conclusions from the different applications are discussed.KeywordsGenetic AlgorithmPareto FrontMultiobjective OptimizationPareto FrontierStandard Genetic AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.