Title :
Parameter setting for knowledge evolution algorithm
Author :
Xu Mengjun ; Ma Huimin
Author_Institution :
Bus. Sch., Univ. of Shanghai for S & T, Shanghai, China
Abstract :
Knowledge evolution algorithm (KEA) is a new optimization algorithm relies on the mechanism of knowledge evolution, and a series of parameters used in the algorithm play an important role in the optimization performance. Based on the knapsack problems, basic principles of parameter setting are proposed by using simulation experiments, which are beneficial to further application and promotion of the algorithm.
Keywords :
artificial intelligence; knapsack problems; optimisation; knapsack problems; knowledge evolution algorithm; optimization algorithm; optimization performance; parameter setting principle; Aerospace electronics; Algorithm design and analysis; Business; Convergence; Educational institutions; Optimization; Standards; Knapsack Problem; Knowledge Evolution Algorithm; Optimization; Parameter Setting;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244386
