Genetic algorithms for the synthesis optimization of a set of irredundant diagnostic tests in the intelligent system

• Main Authors: Anna E. Yankovskaya, Alex M. Bleikher
• Format: Article
• Language: English
• Published: Institute of Mathematics and Computer Science of the Academy of Sciences of Moldova 2001-12-01
• Series: Computer Science Journal of Moldova
• Online Access: http://www.math.md/nrofdownloads.php?file=/files/csjm/v9-n3/v9-n3-(pp336-349).pdf

Method of the synthesis optimization of a set of irredundant diagnostic tests with genetic algorithms used to solve problems of large dimension is suggested. The idea of creating the irredundant partial implication matrix sectionalized by classification mechanisms lies in the base; creating a set of irredundant diagnostic tests is based on revealing certain kinds of regularities with the use of genetic transformations. All the obligatory and non-informative features are not used in genetic transformations. Procedures of selection able-to-compete individuals from populations, decision making concerning the object under examination of each able-to-compete individual from populations, and organizing of voting on the set of these individuals are suggested. In order to solve these problems the intelligent system is used.