Abstract
A technique is presented to assist in the design of stratified random sampling plans via a genetic algorithm approach. When limited resources are available, researchers must often be thrifty in their attempt to find a minimized variance estimate of the mean, subject to the cost constraints of collecting data. The research presented here exploits the artificial intelligence technique of genetic algorithms to find stratified sampling plans, and preliminary results are near-optimal.
| Original language | English (US) |
|---|---|
| Pages | 1663-1668 |
| Number of pages | 6 |
| State | Published - 2003 |
| Event | 34th Annual Meeting of the Decision Sciences Institute - Washington, DC, United States Duration: Nov 22 2003 → Nov 25 2003 |
Other
| Other | 34th Annual Meeting of the Decision Sciences Institute |
|---|---|
| Country/Territory | United States |
| City | Washington, DC |
| Period | 11/22/03 → 11/25/03 |
Keywords
- Heuristics
- Mathematical Programming/Optimization
- Simulation
ASJC Scopus subject areas
- Management Information Systems
- Hardware and Architecture