What is a characteristic of the genetic algorithm for hyperparameter optimization?

The characteristic of a genetic algorithm for hyperparameter optimization is that it evolves parameter combinations using a fitness function. This approach mimics the process of natural selection, where candidate solutions (or parameter combinations) are treated as individuals in a population. Each individual is evaluated using a fitness function, which measures how well it performs according to some criteria, such as prediction accuracy or loss. The most promising candidates are then selected for reproduction; they can undergo processes such as crossover and mutation to create new parameter combinations for the next generation. This iterative process allows the algorithm to explore the space of hyperparameter configurations more effectively than simply searching through the space linearly or exhaustively.

In contrast, a linear search would simply evaluate parameters in a straightforward manner without considering the interaction between them, while a brute-force method would exhaustively check every possible combination, which can be computationally impractical. Manual tuning would involve a labor-intensive, trial-and-error approach, lacking the automated, evolutionary benefits that genetic algorithms provide. Thus, the use of a fitness function in evolving parameter combinations is a defining feature of genetic algorithms in hyperparameter optimization.