Illinois Genetic Algorithms Laboratory

Yu, T.-L., Goldberg, D. E., Yassine, A., Chen, Y.-P. (2003)
TR No.: 2003007 | Download PDF | Download PS

Abstract:
This paper proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. A population sizing model is also built for the DSMDGA. Three cases: tight,
loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA by using the extracted BB information.