We introduce the modular genetic algorithm (MGA). The modular genetic algorithm is a search algorithm designed for a class of problems pervasive throughout nature and engineering: problems with modularity and regularity in their solutions. We hypothesize that genetic search algorithms with explicit mechanisms to exploit regularity and modularity on the problem space would not only outperform conventional genetic search, but also scale better for this problem class. In this paper we present experimental evidence in support of our hypothesis. In our experiments, we compare a limited version of the modular genetic algorithm with a canonical genetic algorithm (GA) applied to the checkerboard-pattern discovery problem for search spaces of sizes 232 , 2128, and 2512 . We observe that the MGA significantly outperforms the GA for high complexities. More importantly, while the performance of the GA ch'ops 22.50% when the complexity of the problem increases, the MGA performance ch'ops only 11.38%. These results indicate that the MGA has a strong scalability property for problems with regularity and modularity in their solutions.