Stochastic hill climbing vs first-choice hill climbing algorithms

Question

What is the difference between stochastic hill climbing and first-choice hill climbing algorithms?

Answer 1

Hill Climbing Search Algorithm is one of the family of local searches that move based on the better states of its neighbors. Stochastic Hill Climbing chooses a random better state from all better states in the neighbors while first-choice Hill Climbing chooses the first better state from randomly generated neighbors.

First-Choice Hill Climbing will become a good strategy if the current state has a lot of neighbors.

Answer 2

I am quoting from Artificial Intelligence: A Modern Approach (3rd ed.) (2010) by Russell, Norvig

Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move. This usually converges more slowly than steepest ascent, but in some state landscapes, it finds better solutions. First-choice hill climbing implements stochastic hill climbing by generating successors randomly until one is generated that is better than the current state. This is a good strategy when a state has many (e.g., thousands) of successors.

So First-choice hill climbing is a special kind of stochastic hill climbing.