Genetic algorithms: from evolutionary theory to industrial efficiency
Keywords:
bioprocesses, optimization, computational simulationAbstract
Process optimization has looked to nature for inspiration to overcome the limitations of traditional mathematical methods. This bio-inspired approach has been fundamental in addressing problems where conventional computation proves insufficient. Genetic algorithms (GAs) allow for the efficient exploration of vast search spaces, strengthening decision-making and optimizing resources without relying solely on physical experimentation. This article explores the theoretical origins of GAs and demonstrates their potential through two case studies. It shows how these bio-inspired tools are successfully applied to solve complex problems in industry.
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