-based intensification and post-optimization techniques using path-relinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.

algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge

be computed. KEYWORDS: image databases, content-based retrieval, image indexing, similarity retrieval, spatial query. 1 INTRODUCTION In this paper we investigate a new content-based image query system that enables querying by image regions and spatial layout. VisualSEEk is a hybrid system

reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and-loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This paper reviews the terminology used for phylogenetic networks and covers

implementation of the if-then-else (ITE) operator. A hash table is used to maintain a strong carwnical form in the ROBDD, and memory use is improved by merging the hash table and the ROBDD into a hybrid data structure. A memory funcfion for the recursive ITE algorithm is implemented using a hash-based cache

management. We formalize the notion of a secure group as a triple (U; K;R) where U denotes a set of users, K a set of keys held by the users, and R a user-key relation. We then introduce key graphs to specify secure groups. For a special class of key graphs, we present three strategies for securely

that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival

, and the strategies used Effort sponsored by the Defense Advanced Research Projects Agency, and Air Force Research Laboratory, Air Force Materiel Command,USAF, under agreement numbers F30602-94-C-0253, F3060297 -2-0021, F30602-98-2-0163, F30602-99-C-0174, F30602-00-2-0608, and N66001-00-8945; by the Air Force Office