On the Far from Most String Problem, One of the Hardest String Selection Problems

This paper describes the far from most string problem, one of the computationally hardest string selection problems that has found its way into numerous practical applications, especially in computational biology and bioinformatics, where one is interested in computing distance/proximity among biological sequences, creating diagnostic probes for bacterial infections and/or in discovering potential drug targets. With special emphasis on the optimization and operational research perspective, this paper studies the intrinsic properties of the problem and overviews the most popular solution techniques, including some recently proposed heuristic and metaheuristic approaches. Future directions are discussed in the last section

L'intégration des joueurs de football africains en Suisse

A GRASP with path-relinking for finding good-quality solutions of the weighted maximum satisfiability problem (MAX-SAT) is described in this paper. GRASP, or Greedy Randomized Adaptive Search Procedure, is a randomized multi-start metaheuristic, where at each iteration locally optimal solutions are constructed, each independent of the others. Previous experimental results indicate its effectiveness for solving weighted MAX-SAT instances. Path-relinking is a procedure used to intensify the search around good-quality isolated solutions that have been produced by the GRASP heuristic. Experimental comparison of the pure GRASP (without path-relinking) and the GRASP with path-relinking illustrates the effectiveness of path-relinking in decreasing the average time needed to find a good-quality solution for the weighted maximum satisfiability problem