Many vision problems involve the detection of the boundary of an object, like a hand, or the tracking of a one-dimensional structure, such as a road in an aerial photograph. These problems can be formulated in terms of Bayesian probability theory and hence expressed as optimization problems on trees or graphs. These optimization problems are difficult because they involve search through a high ...

One of the key factors in the eeciency of backtracking algorithms is the rule they use to decide on which variable to branch next (namely, the variable ordering heuristics). In this paper, we give a formulation of dynamic variable ordering heuristics that takes into account the properties of the neighborhood of the variable.

Meta-heuristics are commonly applied to solve various global optimization problems. In order make the meta-heuristics performing a search, balancing their exploration and ability is still an open avenue. This manuscript proposes novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Learning), inspired by partial centroid. PCOBL aims improve performance through maintaining eff...

This document elaborates on the generic implementation one of main heuristics algorithms verified through its quick application to a biology problem requiring find out an optimal sequences tree topology. In order solve this problem, categorized as Non-Polynomial Hard (NP-Hard), “to minimize differences between given (leaf) and/or derived (parent) sequences”, many popular methods are used. “The ...

Making inferences with a deep neural network on batch of states is much faster GPU than making one state after another. We build this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead either search tree or transposition table we use both in the same algorithm. The contains results while statistics Search. also analyze multiple heuristics that improve searc...

The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristic-biased stochastic sampling (HBSS) and value-biased stochastic sampling (VBSS), wherein a heuristic is used to bias a stochastic policy for choosing among alternative branches in the search...

Multimedia applications involving digital audio and/or digital video transmissions require strict QoS constraints (end-to-end delay bound, bandwidth availability, packet loss rate, etc.) to be met by the network. To guarantee the real-time delivery of packets satisfying these constraints, a real-time channel 4] needs to be established before the transmission of packets of a connection can begin...

The Traveling Salesman Problem (TSP) is a well-known NPhard combinatorial optimization problem, for which a large variety of evolutionary algorithms are known. However, these heuristics fail to find solutions for large instances due to time and memory constraints. Here, we discuss a set of edge fixing heuristics to transform large TSP problems into smaller problems, which can be solved easily w...

Randomized search heuristics like local search, tabu search, simulated annealing or all kinds of evolutionary algorithms have many applications. However, for most problems the best worst-case expected run times are achieved by more problem-specific algorithms. This raises the question about the limits of general randomized search heuristics. Here a framework called black-box optimization is dev...