【摘要】 为了避免小生境遗传算法存在的早期成熟和陷入局部极值点等问题,提出了一种改进的小生境遗传算法.该算法基于自适应交叉概率算子和变异算子,根据进化代数和群体的适应值,动态调整各个个体的交叉概率和变异概率,并在变异量的确定上引入了梯度的概念.通过在Shubert函数的全局最优化问题上的验证,并与常规遗传算法和小生境遗传算法比较,改进后的算法提高了搜索速度,能有效跳出局部极小值,并搜索到全局最优值.更多还原

【Abstract】 In order to avoid premature convergence and occurance of minimal deceptive problems, an improved niche genetic algorithm (NGA) is presented. The algorithm is based on the adaptive mutation operator and crossover operator that adjusts the crossover rate and frequency of mutation of each individual, and adopts the gradient of the individual to decide their mutation value. This approach is used in Shubert function optimization. Through comparisons to GA and NGA, the result of improved algorithms shows its feasibility and effectivity.更多还原