Abstract
In this paper we describe a hybrid strategy for solving combinatorial optimisation problems, obtained by coupling a local search method to an evolutionary algorithm, and we provide an application to a particular variant of the vehicle routing problem. The local search method has been devised specifically for this class of problems. It is based on a composite neighbourhood, which is searched iteratively up to the point where no further improvements are made. The evolutionary structure is the niche search, an algorithm based on the evolution of several independent niches. Niches whose individuals' fitness is good remain, and the others tend to be replaced. The separation of the population into niches allows for a good compromise between intensive search (inside each niche) and diversification (through the separation between the niches). We also describe how we integrate specific problem knowledge into an evolutionary structure, in order to achieve a high performance optimisation algorithm. All the steps that we consider necessary are described in detail: finding an appropriate representation, determining what is a relevant neighbourhood, setting up a local search method and finally integrating; the local search into an evolutionary algorithm.

Abstract
This paper presents and analyzes an incremental system for clustering streaming time series. The Online Divisive-Agglomerative Clustering (ODAC) system continuously maintains a tree-like hierarchy of clusters that evolves with data, using a top-down strategy. The splitting criterion is a correlation-based dissimilarity measure among time series, splitting each node by the farthest pair of streams. The system also uses a merge operator that reaggregates a previously split node in order to react to changes in the correlation structure between time series. The split and merge operators are triggered in response to changes in the diameters of existing clusters, assuming that in stationary environments, expanding the structure leads to a decrease in the diameters of the clusters. The system is designed to process thousands of data streams that flow at a high rate. The main features of the system include update time and memory consumption that do not depend on the number of examples in the stream. Moreover, the time and memory required to process an example decreases whenever the cluster structure expands. Experimental results on artificial and real data assess the processing qualities of the system, suggesting a competitive performance on clustering streaming time series, exploring also its ability to deal with concept drift.

Abstract
We introduce two formulations for training support vector machines, based on considering the L-1 and L-infinity norms instead of the currently used L-2 norm, and maximising the margin between the separating hyperplane and each data sets using L-1 and L-infinity distances. We exploit the geometrical properties of these different norms. and propose what kind of results should be expected for them. Formulations in mathematical programming for linear problems corresponding to L-1 and L-infinity norms are also provided, for both the separable and non-separable cases. We report results obtained for some standard benchmark problems. which confirmed that the performance of all the formulations is similar. As expected, the CPU time required for machines solvable with linear programming is much shorter.

Abstract
In this paper we describe niche search, a genetic-based optimisation approach which is characterised by an evolutionary search on two layers: the individual layer (which is comparable to search described in other genetic algorithms), and the niche layer. Neither of these searches is directed: both individuals and niches evolve based on the selection of the fittest. The numerical results obtained by niche search are quite promising, as our implementation has successfully handled all the tests carried out. The computational performance is considerably better than that of other algorithms of the same family analysed in the literature.

Abstract
In this paper we deal with the optimisation problem involved in determining the maximal margin separation hyperplane in support vector machines. We consider three different formulations, based on L-2 norm distance (the standard case), L-1 norm, and L-infinity norm. We consider separation in the original space of the data (i.e., there are no kernel transformations). For any of these cases, we focus on the following problem: having the optimal solution for a given training data set, one is given a new training example. The purpose is to use the information about the solution of the problem without the additional example in order to speed up the new optimisation problem. We also consider the case of reoptimisation after removing an example from the data set. We report results obtained for some standard benchmark problems.

Abstract
This paper presents the Stacking Problem, a hard combinatorial optimization problem concerning handling and storage of items in a warehouse, where they are handled by a crane and organized into stacks. We define the problem, study its complexity class, and present a mathematical programming model to solve it. In order to tackle medium- or large-scale instances, we propose a simulation-based algorithm using semi-greedy construction heuristics. This simple approach allows for multiple constructions, finding solutions within reasonable time even for large instances. Three semi-greedy heuristics are proposed and compared in an extensive computational experiment, where we study the relation between the number of constructions and the best solution obtained using each heuristic.