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An admissible heuristic is used to estimate the cost of reaching the goal state in an informed search algorithm. In order for a heuristic to be admissible to the search problem, the estimated cost must always be lower than or equal to the actual cost of reaching the goal state. The search algorithm uses the admissible heuristic to find an ...
In such search problems, a heuristic can be used to try good choices first so that bad paths can be eliminated early (see alpha–beta pruning). In the case of best-first search algorithms, such as A* search, the heuristic improves the algorithm's convergence while maintaining its correctness as long as the heuristic is admissible.
Matheuristics [1] [2] are problem agnostic optimization algorithms that make use of mathematical programming (MP) techniques in order to obtain heuristic solutions. Problem-dependent elements are included only within the lower-level mathematic programming, local search or constructive components.
The algorithm continues until a removed node (thus the node with the lowest f value out of all fringe nodes) is a goal node. [b] The f value of that goal is then also the cost of the shortest path, since h at the goal is zero in an admissible heuristic. The algorithm described so far only gives the length of the shortest path.
Admissible decision rule, in statistical decision theory, a rule which is never dominated; Admissible rule, in logic, a type of rule of inference; Admissible heuristic, in computer science, is a heuristic which is no more than the lowest-cost path to the goal; Admissible prime k-tuple, in number theory regarding possible constellations of prime ...
Comparison of an admissible but inconsistent and a consistent heuristic evaluation function. Consistent heuristics are called monotone because the estimated final cost of a partial solution, () = + is monotonically non-decreasing along any path, where () = = (,) is the cost of the best path from start node to .
The satisfiability problem, also called the feasibility problem, is just the problem of finding any feasible solution at all without regard to objective value. This can be regarded as the special case of mathematical optimization where the objective value is the same for every solution, and thus any solution is optimal.
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).