t-GRASP, a targeted GRASP for assessing neuronal connectivity

Using Experience for Assessing Grasp Reliability

Autonomous manipulation is a key issue for a humanoid robot. Here, we are interested in a vision-based grasping behavior so that the robot can deal with previously unknown objects in real time and in an intelligent manner. Starting from a number of feasible candidate grasps, we focus on the problem of predicting their reliability using the knowledge acquired in previous grasping experiences. A ...

Grasp.

A GRASP for graph planarization

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. In this paper, we describe a GRASP for the graph planarization problem, extending the heuristic of Goldschmidt and Takvorian [Networks, v. 24, pp. 69–73, 1994]. We review basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for graph planarization is...

A Grasp for Qap 214

Du sequentiel au parallele: la recherche aborescente a la programmation quadra-tique en variables 0 et 1, 1987. Th ese d'Etat. 33] , A parallel branch and bound algorithm for the quadratic assignment problem, Discrete A GRASP FOR QAP 21 4. Concluding remarks. In this paper, we discussed aspects of a GRASP implementation for solving the QAP. The algorithm was tested on a broad range of test prob...

A GRASP Algorithm for Clustering

We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization for getting initial solutions and K-Means algorithm as a local search algorithm. We compare it with some typical initialization methods: Random, Forgy, Macqueen a...