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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)
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 paper presents a heuristic-search algorithm called Memory-bounded Anytime Window A∗ (MAWA∗), which is complete, anytime, and memory bounded. MAWA∗ uses the window-bounded anytime-search methodology of AWA∗ as the basic framework and combines it with the memory-bounded A∗ -like approach to handle restricted memory situations. Simple and efficient(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)
We present a profile based meta-reasoning model for parameter control of CAD algorithms working under constrained run-time. We also propose a unified framework, that can take informed decision about the time allocation and parameter adaptation of the algorithm, where there is no hard run-time constraints, instead the quality-time tradeoff is expressed by a(More)