In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to compact an exponential number of an potential hypotheses, and because better hypothesis alignment may benefit constructing better quality confusion networks, it is natural to add more useful information to improve alignment results.
evolutionary algorithm (Deb et al., 2000;Deb et al., 2002), Tabu search (Hansen, 1997), ants colony (Engelbrecht, 2005), and simulated annealing (Serafini, 1994).