This is quite different from searching for a sequence of states from an initial state to a goal, although it is true that the entire sequence of states can be treated as a single object which is refined by static optimization algorithms. The book refers to these types of problems as having a path cost of zero and explores them in sections 3.7 and 4.4 and exercises 3.7 and 3.8. These problems are characterized by requiring a sequence of states in the problem solution, having an initial state, a set of goal states or a termination condition, and there are costs for the state transitions in the solution sequence. Efficient algorithms for this class of problems are typically specialized for finding sequences of states. Forward search builds a tree from the initial state until the goal set is reached or the termination condition is satisfied.
Backward search builds a tree from the goal set until the initial state is reached.
Bidirectional search performs forward and backward search simultaneously until the trees meet. So, a breadth first search can be forward, backward, or bidirectional, for example. Keep in mind that there is generally a tradeoff between algorithm generality and algorithm performance. Expand best N children at each node. Compromise between breadth and depth first search.