CAJ | 학술논문

为了提高遗传算法的搜索效率和收敛速度，本文给出了一种新的改进的遗传算法。该算法采用对群的优化来保持种群的多样性，保留历史最优个体并定期替换最优个体从而使得个体优化，对交叉概率和变异概率采用自适应的概率进行优化。通过对目标函数的测试表明，将改进遗传算法与基本遗传算法相比较，在函数最优值，平均收敛代数方面取得了令人满意的效果。
위료제고유전산법적수색효솔화수렴속도，본문급출료일충신적개진적유전산법。해산법채용대군적우화래보지충군적다양성，보류역사최우개체병정기체환최우개체종이사득개체우화，대교차개솔화변이개솔채용자괄응적개솔진행우화。통과대목표함수적측시표명，장개진유전산법여기본유전산법상비교，재함수최우치，평균수렴대수방면취득료령인만의적효과。
In order to improve the search efficiency and convergence speed of genetic algorithm,A new improved genetic algorithm is presented. The algorithm adopts the optimization of group to keep the diversity of population,it also keep the history optimal individual and regularly replace the best individual so as to make the individual optimization,it were optimized by the probability of adaptive to change the crossover probability and mutation probability. Based on the objective function tests,the improved genetic algorithm is compared with basic genetic algorithm,it has achieved satisfactory effect in the optimal value of the function and average convergence algebra.