Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems.

摘要

This study evaluates the efficacy of the continuous GA algorithm in solving two complex problems. The queuing network design problem has random processes and the decisions are subject to a cost constraint. Therefore, a simulation program is run to determine the fitness of each solution in population. The second problem is a large-scale gene-clustering problem in which thousands of genes have to be grouped into a fixed, small number of clusters on the basis of similarity. Clustering of gene expressions reduces the unmanageable volume of data into small number of sets that are of great interest to the biologists. For these problems, the GA algorithm produces good results compared to the alternative methods. The GA can also handle the same class of problems without the restrictive assumptions.