Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Clustering and case-based (business) recommenders have thus one thing in common: although similarity is a very im- portant ingredient for both of them, the result of the similar-

So we can combine many lexical and semantic information into one similarity matrix and train a machine learning model for trying to solve ontology and schema matching problems..

In order to produce reasonable outcomes in acceptable time, a hybrid imperialist competitive algorithm and tabu search is proposed to solve the problem.. Numerical experiments

The iterated greedy algorithm solves 80% of hard graph isomorphism problem instances (regular graphs having 100 vertices, see [11]) in less than 10 seconds (on a Pentium IV 2Ghz,

Nous nous intéressons dans cette partie à l’aspect apprentissage de clauses pour résoudre le problème de car-sequencing en utilisant l’algorithme de filtrage de AtMostSeqCard que

Scores obtained demonstrate that models trained at the harder levels of difficulty are more ro- bust to noise and achieve better results.. As before, also in this case MLP is,

This is achieved through a parameterization of similarity functions by convex combinations of sparse rank-one matrices, together with the use of a greedy approximate

The total number of features (# Features), the size of the training |T | and validation |V | sets (i.e., number of observations), the run- time (in seconds) needed by SVM to build