Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search
The algorithm selection problem asks to select the best algorithm for a given problem. In the companion paper (Everitt and Hutter 2015b), expected runtime was approximated as a function of search depth and probabilistic goal distribution for tree search versions of breadth-first search (BFS) and depth-first search (DFS). Here we provide an analogous analysis of BFS and DFS graph search, deriving expected runtime as a function of graph structure and goal distribution. The applicability of the method is demonstrated through analysis of two different grammar problems. The approximations come surprisingly close to empirical reality.
- Everitt, T., Hutter, M.: A topological approach to Meta-heuristics: analytical results on the BFS vs. DFS algorithm selection problem. Technical report, Australian National University. arXiv:1509.02709[cs.AI] (2015a)
- Everitt, T., Hutter, M.: Analytical results on the BFS vs. DFS algorithm selection problem. In: 28th Australian Joint Conference on Artificial Intelligence, Part I: Tree Search (2015b)Google Scholar
- Kotthoff, L.: Algorithm selection for combinatorial search problems: a survey. AI Magazine, pp. 1–17 (2014)Google Scholar
- Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Boston (1984)Google Scholar
- Peixoto, T.P.: The graph-tool python library. figshare (2015)Google Scholar