<p> Identifying motifs that are "close" to one or more substrings in each sequence in a given set of sequences and hence characterize that set is an important problem in computational biology. The target motif identification problem requires motifs that characterize one given set of sequences but are far from every substring in another given set of sequences. This problem is N P-hard and hence is unlikely to have efficient optimal solution algorithms. In this thesis, we propose a set of modifications to one of the most popular stochastic heuristics for finding motifs, Gibbs Sampling [LAB+93], which allow this heuristic to detect target motifs. We also present the results of four simulation studies and tests on real protein datasets which suggest that these modified heuristics are very good at (and are even, in some cases, necessary for) detecting target motifs.</p> / Thesis / Master of Science (MSc)

Context. The purpose of this research paper is to describe a new approach for solving the disruption in the vehicle routing problem (DVRP) which deals with the disturbance that will occur unexpectedly within the distribution area when executing the original VRP plan. The paper then focuses further on the foremost common and usual problem in real-time scenarios i.e., vehicle-breakdown part. Therefore, the research needs to be accomplished to deal with these major disruption in routing problems in transportation. Objectives. The study first investigates to find suitable and efficient metaheuristic techniques for solving real-time vehicle routing problems than an experiment is performed with the chosen algorithms which might produce near-optimal solutions. Evaluate the performance of those selected algorithms and compare the results among each other. Methods. To answer research questions, firstly, a literature review has been performed to search out suitable meta-heuristic techniques for solving vehicle routing problems. Then based on the findings an experiment is performed to evaluate the performance of selected meta-heuristic algorithms. Results. Results from the literature review showed that the meta-heuristic approaches such as. Tabu Search, Ant Colony Optimization, and Genetic Algorithmare suitable and efficient algorithms for solving real-time vehicle routing problems. The performance of those algorithms has been calculated and compared with one another with standard benchmarks. Conclusions. The performance of a Tabu Search algorithm is best among the other algorithms, followed by Ant Colony Optimization and Genetic Algorithm. Therefore, it has been concluded that the Tabu Search is the best algorithm for solving real-time disruption problems in VRP. The results are similar to the performance comparison of the selected algorithms and standard benchmarks are presented within the research.