Title :
Design Variables Optimization of Mechanical Problems
Author :
Lee, Kuo-Ming ; Tsai, Jinn-Tsong ; Ho, Wen-Hsien ; Liu, Tung-Kuan ; Chou, Jyh-Horng
Author_Institution :
Inst. of Eng. Sci. & Technol., Nat. Kaohsiung First Univ. of Sci. & Technol., Kaohsiung
Abstract :
An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select the better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions.
Keywords :
genetic algorithms; mechanical engineering computing; nonlinear programming; IGA; MDCNLP; crossover operations; design variables optimization; experimental design method; improved genetic algorithm; mechanical problems; mixed-discrete-continuous nonlinear programming; representative chromosomes; Algorithm design and analysis; Biological cells; Computer industry; Design engineering; Design for experiments; Design optimization; Educational technology; Genetic algorithms; Genetic engineering; Research and development;
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
DOI :
10.1109/ICICIC.2008.642
