Many researchers have been working on the greedy algorithms for covering array generation; a framework has been built to integrate most of these greedy methods, and more new approaches can be derived from this framework. However, such a framework is affected by multiple dependent or independent factors, which makes its deployment and optimization very challenging. In this paper, we design Hill ...

Recently, Tsamardinos et al. [2006] presented an algorithm for Bayesian network structure learning that outperforms many state-of-the-art algorithms in terms of efficiency, structure similarity and likelihood. The Max-Min Hill Climbing algorithm is a hybrid of constraint-based and search-and-score techniques, using greedy hill climbing to search a constrained space of possible network structure...

The naïve Bayes classifier is built on the assumption of conditional independence between the attributes given the class. The algorithm has been shown to be surprisingly robust to obvious violations of this condition, but it is natural to ask if it is possible to further improve the accuracy by relaxing this assumption. We examine an approach where naïve Bayes is augmented by the addition of co...

Pattern databases (Culberson & Schae er, 1998) or PDBs, have been proven very e ective in creating admissible Heuristics for single-agent search, such as the A*-algorithm. Haslum et. al proposed, a hill-climbing algorithm can be used to construct the PDBs, using the canonical heuristic. A di erent approach would be to change action-costs in the pattern-related abstractions, in order to obtain t...

We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satis ability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimumpoint to terminate search and restar...

TEM image is rapidly gaining prominence in various fields. Since high resolution TEM image captures huge amount of information, it is important to understand image segmentation techniques on it. The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. To have region based image retri...

This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorporated a hill-climbing step. In the present study we return to the algorithm and provide some the...