Title :
Optimizing lineage information in genetic algorithms for producing superior models
Author :
Boetticher, Gary D. ; Rudisill, Jason
Author_Institution :
University of Houston ¿ Clear Lake, Houston, TX 77058, USA
Abstract :
A lot of research in the area of genetic algorithms (GA) is applied, but little research examines the impact of lineage information in optimizing a GA. Normally, researchers consider primarily elitism, an approach which carries only a very small fixed subset of the population to the next generation, as a lineage strategy. This paper investigates several different lineage percentages (what percent of the population to carry forward) to determine an ideal percentage or range from improving the accuracy of a GA. Several experiments are performed, and all results are statistically validated.
Keywords :
Biological cells; Chromosome mapping; Couplings; Equations; Genetic algorithms; Genetic mutations; Lakes; Learning systems; Noise generators; Testing;
Conference_Titel :
Information Reuse and Integration, 2008. IRI 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV, USA
Print_ISBN :
978-1-4244-2659-1
Electronic_ISBN :
978-1-4244-2660-7
DOI :
10.1109/IRI.2008.4583049