Multiple sequence alignment using genetic algorithm and simulated annealing

RBT-GA: a novel metaheuristic for solving the multiple sequence alignment problem

“…The fact that this GA approach discards many offspring is the main reason for its slow convergence. In [ 32 ], the convergence speed of a GA is increased by combining it with a Simulated Annealing algorithm. The GA in [ 3 ] use quantum mechanics concepts by employing a binary matrix to represent only four chromosomes that are used to solve the problem.…”

Optimization Techniques in Computing Good Quality Solutions to Sequence Alignment Problems

“…Recently, to improve the efficiency and the accuracy of the algorithms several hybrid GAs with SA have been proposed [35,36]. In 2004, Omar et al [35] represent the population as an array of sequences over an alphabet of 20 characters.…”

“…As always, the symbol '-' refers to the gap in the alignment and represents an insertion or a deletion of an amino acid residue. Unlike a pure GA, after this step Omar et al's hybrid algorithm [35] performs a further step, called Aligning Improver that tries to improve alignment quahty from a single solution produced from GA. The size of each population is constant and can be decided by the user.…”

An extended library of hardware modules for genetic algorithms, with applications to DNA sequence matching

“…In 2004, Faizal et al [30] developed a hybrid algorithm which is a combination of a GA and simulated annealing. The GA tries to find new regions of feasible solutions while simulated annealing serves to improvise the solution and prevent convergence to local minima.…”