Metaheuristic Multiple Sequence Alignment Optimisation

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restarting a local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA. After that, ILS will be evaluated and compared to other MSA algorithms by BAliBASE (Thomson et al., 1999), a set of manually refined alignments used in most recent publications of algorithms and in at least two MSA algorithm surveys. The runtime-behaviour will be evaluated using runtime-distributions. The quality of alignments produced by ILS is at least as good as the best algorithms available and significantly superiour to previously published Metaheuristics for MSA, Tabu Search and Genetic Algorithm (SAGA). On the average, ILS performed best in five out of eight test cases, second for one test set and third for the remaining two. A drawback of all iterative methods for MSA is the long runtime needed to produce good alignments. ILS needs considerably less runtime than Tabu Search and SAGA, but can not compete with progressive or consistency based methods, e. g. ClustalW or T-COFFEE