Genetic Master-Slave Algorithm for Haplotype Inference by Parsimony

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotypes. This piece of information makes it possible to perform association studies for the genetic variants involved in multifactorial diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using combinatorial optimization techniques. Recently, several new approaches to the problem were presented. Among them, solvers based on hybrid metaheuristics have been proven to be effective in solving large size instances. In this paper, we present an master-slave hybrid approach, in which a master solver optimize the parameters used by a slave solver for constructing a solution. By testing the algorithm on common Haplotype Inference benchmarks, we show that this approach can produce good quality solutions in a very short execution time.