GA methods also may converge without expert bias in parameters but typically after more time or generations of mutation, crossover, and selection. Why would th

GA methods also may converge without expert bias in parameters but typically after more time or generations of mutation, crossover, and selection. Why would the fitness be lower with mutation, crossover, and selection? Manual optimization is selection: the - possibly triple-blind - researcher biases the analysis by choosing models, hyperparameters, and parameters. This avoids search cost, but just like gradient descent it can result in unexplored spaces where optima may lurk. There's already GA Genetic Algorithms deep space antenna design success story. But there are also dielectric and Rydberg antennae, which don't at all need to be as long as the radio wavelength. How long would that have taken the infinite monkeys GA? /?hnlog: antenna, Rydberg, dielectric: https://westurner.github.io/hnlog/#comment-38054784 https://westurner.github.io/hnlog/#comment-38054784