Computer-chess programs achieve outstanding results at playing chess. However, no existing program can compose adequate chess problems. In this paper, we present a model that is capable of improving the quality of some of the existing chess problems. We have formalized a major part of the knowledge needed for evaluating the quality of chess problems. In the model, we attempt to improve a given ...

A general hill-climbing attack algorithm based on Bayesian adaption is presented. The approach uses the scores provided by the matcher to adapt a global distribution computed from a development set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on a competitive feature-based signature verification system over the 330 users of the MCYT databas...

This paper provides guidelines to design climbers considering a landscape shape under study. In particular, we aim at competing best improvement and first improvement strategies, as well as evaluating the behavior of different neutral move policies. Some conclusions are assessed by an empirical analysis on non-neutral (NK-) and neutral (quantized NK-) landscapes. Experiments show the ability of...

Much human learning appears to be gradual and unconscious, suggesting a very limited form of search through the space of hypotheses. We propose hill climbing as a framework for such learning and consider a number of systems that learn in this manner. We focus on CLASSIT, a model of concept formation that incrementally acquires a conceptual hierarchy, and MAGGIE, a model of skill improvement tha...

This paper presents new results showing that a very simple stochastic hill climbing algorithm is as good or better than more complex metaheuristic methods for solving an oversubscribed scheduling problem: scheduling communication contacts on the Air Force Satellite Control Network (AFSCN). The empirical results also suggest that the best neighborhood construction choices produce a search that i...

This paper proposes a new method to solve certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations. A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is...

Many learning systems search through a space of possible performance elements, seeking an element with high expected utility. As the task of nding the globally optimal element is usually intractable, many practical learning systems use hill-climbing to nd a local optimum. Unfortunately, even this is diicult, as it depends on the distribution of problems, which is typically unknown. This paper a...

The Hermite collocation method of discretization can be used to determine highly accurate solutions to the steady state one-dimensional convection-diffusion equation (which can be used to model the transport of contaminants dissolved in groundwater). This accuracy is dependent upon sufficient refinement of the finite element mesh as well as applying upstream weighting to the convective term thr...

The credit on reduction theory goes back to the work of Lagrange, Gauss, Hermite, Korkin, Zolotarev, and Minkowski. Modern reduction theory is voluminous and includes the work of A. Lenstra, H. Lenstra and L. Lovasz who created the well known LLL algorithm, and many other researchers such as L. Babai and C. P. Schnorr who created significant new variants of basis reduction algorithms. In this p...