Title :
Modified Genetic Algorithm for Parameter Selection of Compartmental Models
Author :
Shah, N.A. ; Moffitt, R.A. ; Wang, M.D.
Author_Institution :
Georgia Inst. of Technol., Atlanta
Abstract :
A modified genetic algorithm has been developed for the task of optimal parameter selection for compartmental models. As a case study, a predictive model of the emerging health threat of obesity in America was developed which incorporated varying levels of three treatment strategies in an attempt to decrease the amount of overweight Americans over a ten-year period. The genetic algorithm was then applied to the task of minimizing the number of overweight persons while minimizing the costs associated with implementing the chosen treatment plans. Throughout repeated trials, the GA was able to converge to consistent, high- scoring treatment strategies after only a few minutes of computation on a desktop PC. This result demonstrates the ability of the modified Genetic Algorithm to effectively perform multivariate, nonlinear, simulation-based optimization routines in a short time.
Keywords :
genetic algorithms; medical computing; patient treatment; compartmental models; health monitoring; modified genetic algorithm; obesity; parameter selection; patient treatment strategies; treatment planning; Ant colony optimization; Biological information theory; Biological system modeling; Biomedical engineering; Evolution (biology); Genetic algorithms; Genetic mutations; Genetic programming; Robustness; Systems biology; Algorithms; Computer Simulation; Humans; Life Style; Models, Genetic; Obesity; United States;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4352243