Title :
Research for hybrid genetic algorithms on optimization of cutting linear stock
Author :
Guang Dong ; Guang Cai Cui
Author_Institution :
Sch. of Comput. Sci. & Technol., Changchun Univ. of Sci. & Technol., Changchun, China
Abstract :
The improved genetic algorithm i.e. hybrid genetic algorithm is applied to solve problem of cutting linear stock, respective corresponding determination methods and algorithms are given for genetic coding, fitness function, initial population generation and genetic operator. Elitist strategy is used in keeping the excellent individuals of population to make the genetic algorithm more effective.
Keywords :
bin packing; genetic algorithms; cutting linear stock; elitist strategy; fitness function; genetic coding; genetic operator; hybrid genetic algorithm; initial population generation; Algorithm design and analysis; Educational institutions; Encoding; Genetic algorithms; Optimization; Raw materials; crossover; genetic code; hybrid genetic algorithms; mutation; optimize cutting stock;
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang, China
Print_ISBN :
978-1-61284-087-1
DOI :
10.1109/EMEIT.2011.6023554
