Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithms
We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combined utilization of evolutionary algorithms and other existing algorithms. The main features of this approach are the heuristic seeding of the initial population, the utilization of a specialized recombination operator, and the use of a majority-voting procedure in order to build a consensus solution. Experimental results provide support for the potential usefulness of this approach.
temporal boolean networks noisy data evolutionary algorithms biocomputation genetic network inference