Improved genetic algorithm based on growth operator and simulation
基于成长算子的改进遗传算法及仿真

By emulating the process of growth in nature and using growth operator, a growth genetic algorithm (GGA) is proposed to overcome the drawbacks of simple GA (SGA) such as slow optimization speed and weak local search ability. A practical realization of growth operator is proposed by making use of the strong local search ability of the hill climbing method. It has been demonstrated that adding the growth operator doesn't change the convergence property of SGA. The simulation result compared with SGA and deterministic crowding GA (DCGA) for function optimization verifies that the growth genetic algorithm facilitates the balance between optimization speed and convergence precision.