This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age. / Department of Computer Science
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:handle/186610 |
| Date | January 1999 |
| Creators | Anderson, Jon K. |
| Contributors | Bagga, Kunwarjay S. |
| Source Sets | Ball State University |
| Detected Language | English |
| Format | v, 124 leaves : ill. ; 28 cm. |
| Source | Virtual Press |
Page generated in 0.0022 seconds