We focus on hybrid solution methods for a large-scale realworld multimodal homecare scheduling (MHS) problem, where the objective is to find an optimal roster for nurses who travel in tours from patient to patient, using different modes of transport. In a first step, we generate a valid initial solution using Constraint Programming (CP). In a second step, we improve the solution using one of th...

This paper presents a new hybrid method for solving constraint optimization problems in anytime contexts. Discrete optimization problems are modelled as Valued CSP. Our method (VNS/LDS+CP) combines a Variable Neighborhood Search and Limited Discrepancy Search with Constraint Propagation to efficiently guide the search. Experiments on the CELAR benchmarks demonstrate significant improvements ove...

In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also pro...

in this paper, the concept of {sl local base with  stratifiedstructure} in $i$-topological vector spaces is introduced. weprove that every $i$-topological vector space has a balanced localbase with stratified structure. furthermore, a newcharacterization of $i$-topological vector spaces by means of thelocal base with stratified structure is given.

The paper presents use of the mini-models method in a classification task. The article briefly describes the method and compares it to the k-nearest neighbor algorithm. The algorithm concentrates only on local query data and uses a data samples only from local neighborhood of the query. The paper presents the results of experiment that compare the effectiveness of mini-models with selected meth...

In this paper, we present the problem of planning the acquisitions performed by a constellation of radar satellites in order to fulfil as well as possible an ocean global surveillance mission. Then, we describe the local search algorithm, inspired by large neighborhood search techniques and knapsack heuristics, that has been specifically designed and implemented to solve daily planning problem ...

We present the newly developed core concept for the Multidimensional Knapsack Problem (MKP) which is an extension of the classical concept for the one-dimensional case. The core for the multidimensional problem is defined in dependence of a chosen efficiency function of the items, since no single obvious efficiency measure is available for MKP. An empirical study on the cores of widely-used ben...

Information acquired early in life is processed faster than information acquired late in life. Moore and Valentine (1998) report naming celebrities' faces follows the same pattern of results. This is problematic for the account of age of acquisition (AoA) based on language development because knowledge of celebrities is acquired after early representations are formed in the phonological lexicon...

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search o...