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Genetic Algorithms mimic natural selection, where the fittest individuals are selected for reproduction in order to produce offspring with similar traits. In this context, each individual is represented by a data structure, such as an array, and its fitness is evaluated using a predefined function. The algorithm iteratively applies genetic operators, like crossover and mutation, on the population of individuals to evolve better solutions over time. Genetic Algorithms are particularly useful in solving optimization problems where traditional methods have limitations due to their complexity or large search spaces. They can be employed in various domains, such as machine learning, engineering design, scheduling, and manufacturing. In the context of Decision Support Systems, Genetic Algorithms can help identify optimal decisions by searching through possible alternatives and evaluating their fitness based on predefined criteria, making them a valuable tool for complex decision-making processes.