A general hill-climbing attack to biometric systems based on a modification of the downhill simplex algorithm is presented. The scores provided by the matcher are used in this approach to adapt iteratively an initial estimate of the attacked template to the specificities of the client being attacked. The proposed attack is evaluated on a competitive feature-based signature verification system o...

This paper describes a system for the unsupervised learning of morphological suffixes and stems from word lists. The system is composed of a generative probability model and hill-climbing and directed search algorithms. By extracting and examining morphologically rich subsets of an input lexicon, the directed search identifies highly productive paradigms. The hill-climbing algorithm then furthe...

The combination of the broad problem searching capabilities of a genetic algorithm with the local maxima location capabilities of a hill climbing algorithm can be a powerful technique for solving classification problems. Producing a number of specialist artificial neural networks, each an expert on one category, can be beneficial when solving problems in which the categories are distinct. This ...

We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., “using a gallon of gasoline is as bad as creating 2 bags of landfill trash”), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum like...

Subset selection problems are relevant in many domains. Unfortunately, their combinatorial nature prohibits solving them optimally in most cases. Local search algorithms have been applied to subset selection with varying degrees of success. This work presents COMPSET, a general algorithm for subset selection that invokes an existing local search algorithm from a random subset and its complement...

This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powe...

Augmenting an existing network with additional links to achieve higher robustness and survivability plays an important role in network design. We consider the problem of augmenting a network with links of minimum total cost in order to make it edge-biconnected, i.e. the failure of a single link will never disconnect any two nodes. A new evolutionary algorithm is proposed that works directly on ...

The finishing train of a hot strip mill has been modelled by using a constant volume element model. The accuracy of the model has been increased by using an Artificial Neural Network (ANN). A non-linear Rank Based Genetic Algorithm has been developed for the optimization of the work roll profiles in the finishing stands of the simulated hot strip mill. It has been compared with eight other expe...

There is a variety of knapsack problems in the literature. Multidimensional 0-1 Knapsack Problem (MKP) is an NP-hard combinatorial optimization problem having many application areas. Many approaches have been proposed for solving this problem. In this paper, an empirical investigation of memetic algorithms (MAs) that hybridize genetic algorithms (GAs) with hill climbing for solving MKPs is prov...

In my youth I used to wander in the mountains. I would gain a "feel" of the terrain and gradually build up a reliable intuition of how to get from here to there and back again. Always, on these expeditions, I would discover special places a tiny area, the only one, where fairy slippers grew; a pool in a rushing stream that was deep enough to swim in. Invariably I would find myself excitedly cli...