Advantages and Disadvantages of the Genetic Algorithm
Explore the advantages and disadvantages of using genetic algorithms for hyperparameter optimization. Learn how they enable global search, handle high-dimensional spaces, and allow parallel evaluation. Understand their sensitivity to initial populations, parameter tuning challenges, and computational complexity to effectively apply them in machine learning models.
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Advantages of the genetic algorithm
Global search capability: Traditional hyperparameter optimization methods, such as grid search and random search, are often susceptible to getting stuck in local optima. This is because they typically explore the hyperparameter space in a sequential manner, starting from a single point. A genetic algorithm, on the other hand, is a population-based algorithm that maintains a set of candidate solutions at all times. This allows it to explore multiple ...