Abstract: Genetic algorithm is widely used in optimization
problems for its excellent global search capabilities and highly parallel
processing capabilities; but, it converges prematurely and has a poor
local optimization capability in actual operation. Simulated annealing
algorithm can avoid the search process falling into local optimum. A
hybrid genetic algorithm based on simulated annealing is designed by
combining the advantages of genetic algorithm and simulated
annealing algorithm. The numerical experiment represents the hybrid
genetic algorithm can be applied to solve the function optimization
problems efficiently.