A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states rather than by modifying a single state. GAs begin with a set of k randomly generated states, called the population. Each state, or individual, is represented as a string over a finite … Read more
The local beam search algorithm keeps track of k states rather than just one. It begins with k randomly generated states. At each step, all the successors of all k states are generated. If any one is a goal, the algorithm halts. Otherwise, it selects the k best successors from the complete list and repeats … Read more
Hill-climbing search is simply a loop that continually moves in the direction of increasing value—that is, uphill. It terminates when it reaches a “peak” where no neighbor has a higher value. The algorithm does not maintain a search tree, so the data structure for the current node need only record the state and the value … Read more
Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function To understand local search, we find it useful to consider the state-space landscape . A landscape has both “location” (defined by the state) and “elevation” (defined by the value of … Read more
The most widely known form of best-first search is called A* search (pronounced “A-star SEARCH search”). It evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost to get from the node to the goal f(n) = g(n) + h(n) Since g(n) gives the path cost from the start node … Read more
Bidirectional search is implemented by replacing the goal test with a check to see whether the frontiers of the two searches intersect; if they do, a solution has been found. (It is important to realize that the first such solution found may not be optimal, even if the two searches are both breadth-first; some additional … Read more
Depth-first search always expands the deepest node in the current frontier of the search tree The search proceeds immediately to the deepest level of the search tree, where the nodes have no successors. As those nodes are expanded, they are dropped from the frontier, so then the search “backs up” to the next deepest node … Read more
Breadth-first search is a simple strategy in which the root node is expanded first, then all the successors of the root node are expanded next, then their successors, and so on. In general, all the nodes are expanded at a given depth in the search tree before any nodes at the next level are expanded … Read more
We can evaluate an algorithm’s performance in four ways in Artificial intelligence Completeness Optimality Time complexity Space complexity Time and space complexity are always considered with respect to some measure of the problem difficulty. In theoretical computer science, the typical measure is the size of the state space graph, |V | + |E|, where V … Read more