02496nam a2200289 a 450000100080000000500110000800800410001910000170006024501230007726001730020050000200037352015540039365000220194765000100196965000130197965000130199265000220200565000120202765300240203965300240206365300220208765300250210965300190213470000130215370000210216670000190218721783062025-08-27       2025    bl uuuu     u00u1 u    #d1 aALVES, R. G.  aProposal for a genetic algorithm-based approach to optimize light spectrum in vertical farming.h[electronic resource]  aIn: WORKSHOP ON SEMICONDUCTORS AND MICRO & NANO TECHNOLOGY, 29., 2025, São Bernardo do Campo. Proceedings [...] São Bernardo do Campo: Centro Universitário FEIc2025  aSEMINATEC 2025.  aAgriculture is crucial for global food security and economic development but faces significant challenges due to resource constraints and a global population that keeps increasing. Vertical farming, with its potential to optimize food production in land-scarce and environmentally stressed regions, emerges as a viable solution. However, the high-energy demands for lighting in vertical farms significantly impact operational expenses. This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energyefficient and productive vertical farming practices, supporting the broader goal of sustainable agricultural development.  aEnergy efficiency  aFarms  aGenetics  aLighting  aEnergia Elétrica  aFazenda  aAlgoritmo genético  aArtificial lighting  aGenetic algorithm  aLight Use Efficiency  aVertical farms1 aLIMA, F.1 aGUEDES, I. M. R.1 aGIMENEZ, S. P.