Orthogonal Genetic Algorithm and its Application in Function Optimization
Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive orthogonal local search to prevent local convergence to form a new orthogonal evolutionary algorithm. Through the series of numerical experiments, proved the efficiency of the new algorithm.
Dongye Sun, Wen-Pei Sung and Ran Chen
H. M. Liu et al., "Orthogonal Genetic Algorithm and its Application in Function Optimization", Applied Mechanics and Materials, Vols. 121-126, pp. 4528-4531, 2012