Understanding genetic algorithms
GAs are popular heuristic search methods that are inspired by Charles Darwin’s theory of natural selection. Unlike SA, which is classified as a single-point-based heuristic search method, GAs are categorized as population-based methods since they maintain a group of possible candidate solutions instead of just a single candidate solution at each trial. As a hyperparameter tuning method, you are recommended to utilize a GA when each trial doesn’t take too much time and you have enough computational resources, such as parallel computing resources.
To have a better understanding of GAs, let’s start with a simple example. Let’s say we have a task to generate a pre-defined target word based on only a collection of words that are built from 26 alphabet letters in lowercase. For instance, the target word is “big,” and we have a collection that consists of the words “sea,” “pig,” “...