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Incremental heuristic searches try to reuse their previous search efforts whenever these are available. As a result, they can often solve a sequence of similar planning problems much faster than planning from scratch. State-of-the-art incremental heuristic searches such as LPA*, D* and D* Lite all work by propagating cost changes to all the states on the(More)
This work presents an iterative anytime heuris-tic search algorithm called Anytime Window A* (AWA*) where node expansion is localized within a sliding window comprising of levels of the search tree/graph. The search starts in depth-first mode and gradually proceeds towards A* by increment-ing the window size. An analysis on a uniform tree model provides(More)
The performance of heuristic search based planners depends heavily on the quality of the heuristic function used to focus the search. These algorithms work fast and generate high-quality solutions, even for high-dimensional problems, as long as they are given a well-designed heuristic function. On the other hand, their performance can degrade considerably(More)
The rate of convergence of evolutionary algorithms (EAs) is strongly influenced by the choice of certain parameters, such as population size [1], and mutation [2–4] and crossover probabilities [5], collectively termed as control parameters of the algorithm. In the past, a considerable amount of effort has been put to devise strategies for choosing a good of(More)