Improved genetic algorithm for mixed-discrete-continuous design optimization problems
Kuo-Ming Lee, Jinn‐Tsong Tsai, Tung-Kuan Liu, Jyh‐Horng Chou · Engineering Optimization · 2010
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 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. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.