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A variety of general optimization algorithms commonly used in computer science have also been applied to the multiple sequence alignment problem.

Hidden Markov models have been used to produce probability scores for a family of possible multiple sequence alignments for a given query set ; although early HMM-based methods produced underwhelming performance, later applications have found them especially effective in detecting remotely related sequences because they are less susceptible to noise created by conservative or semiconservative substitutions.

Genetic algorithms and simulated annealing have also been used in optimizing multiple sequence alignment scores as judged by a scoring function like the sum-of-pairs method.

More complete details and software packages can be found in the main article multiple sequence alignment.