论文标题
动态优化问题中的性别遗传算法
Gender Genetic Algorithm in the Dynamic Optimization Problem
论文作者
论文摘要
描述了使用性别遗传算法优化快速过程的一般方法。它与更传统的遗传算法的不同之处在于，将人工种群分为两个性别。与另一个子集中的女性相比，男性亚群经历了大突变和更强烈的选择。这种分离允许将整个人群的快速适应性结合在一起，因为男性亚群的变化与女性部分的适应性固定在一起。与通常的性别遗传算法相比，观察到了其他单个学习以Boldwin效应形式在寻找最佳溶液中的效果的优势。作为具有Boldwin效应的性别遗传算法的有前途的应用，熄灭自然火的动力学。
A general approach to optimizing fast processes using a gender genetic algorithm is described. Its difference from the more traditional genetic algorithm it contains division the artificial population into two sexes. Male subpopulations undergo large mutations and more strong selection compared to female individuals from another subset. This separation allows combining the rapid adaptability of the entire population to changes due to the variation of the male subpopulation with fixation of adaptability in the female part. The advantage of the effect of additional individual learning in the form of Boldwin effect in finding optimal solutions is observed in comparison with the usual gender genetic algorithm. As a promising application of the gender genetic algorithm with the Boldwin effect, the dynamics of extinguishing natural fires is pointed.
