An informed search uses domain-specific information to select which path to explore. For each node n there is a heuristic function h(n) that give the estimated cost from that node to the goal.
Greedy best-first search will always expand the node that looks to be the closest to the goal, i.e. the one with the smallest f.
f(n) = h(n)
Not complete, not admissible.
The evaluation function for A search for node n considers g(n), which is the cost from the start to node n.
f(n) = g(n) + h(n)
Not complete, not admissible.
A* is the same as A search, but must satisfy h(n) ≤ h*(n) where h*(n) is the true cost from n to the goal.
A* is complete and admissible.
IDA* performs DFS with a limit on f(n), incrementing the limit until the goal is found.
IDA* is complete and optimal but generally more costly than A*.
In beam search, the priority queue has a fixed size k, so only the k best nodes are kept as candidates for expansion, the rest are thrown away.
Not complete, not admissible.