a difficult problem to solve in gep is analyzed that is the contradiction between algorithm convergence and population diversity and the premature phenomena.
分析了经典gep在保持种群多样性和全局收敛性之间的矛盾以及「早熟」现象产生的原因。
the algorithm is deduced and difference equation of parameter error given by which the algorithm convergence is proved theoretically.
对该算法进行了推导,并给出了参数误差的差分方程,在理论上证明了算法的收敛性。
for accelerating the algorithm convergence and avoiding the local optimization, an individual learning mechanism is often applied to generic algorithm to improve algorithm performance.
在遗传算法中引入个体学习机制能够提高算法的性能,避免算法收敛过慢或陷入局部最优。
in the meantime, the mutation operator is brought in the gradient direction to accelerate the algorithm convergence speed.