A framework of grid-oriented genetic algorithms for large-scale optimization in bioinformatics

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the grid, named "grid-oriented genetic algorithms (GOGAs)", and actually "gridify" a GA for estimating genetic networks, which is being developed by our group, in order to examine usability of the proposed GOGA framework. We also evaluate the scalability of the "gridified" GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory.