Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions. Earlier researchers worked with constant probabilities of GA control operators like crossover, mutation etc. for tuning the optimization in specific domains. Recent advancements in this field witnessed adaptive approach in probability determination. In Adaptive mutation primarily poor individuals are utilized to explore state space, so mutation probability is usually generated proportionally to the difference between fitness of best chromosome and itself (fMAX - f). However, this approach is susceptible to nature of fitness distribution during optimization. This paper presents an alternate approach of mutation probability generation using chromosome rank to avoid any susceptibility to fitness distribution. Experiments are done to compare results of simple genetic algorithm (SGA) with constant mutation probability and adaptive approaches within a limited resource constraint for unimodal, multimodal functions and Travelling Salesman Problem (TSP). Measurements are done for average best fitness, number of generations evolved and percentage of global optimum achievements out of several trials. The results demonstrate that the rank-based adaptive mutation approach is superior to fitness-based adaptive approach as well as SGA in a multimodal problem space.

翻译：以往的基因变异性主要用于优化单式和多式功能。早期研究人员与大会控制操作员如交叉、突变等的常有概率一起工作,以调整特定领域的优化。该领域最近的进展见证了概率测定的适应性方法。适应性变异性主要是穷人用来探索国家空间,因此,突变概率通常与最佳染色体和自身(fMAX-f)的适合性之间的差别成正比。然而,这种方法在优化期间容易发生健康分布的性质。本文介绍了一种突变概率生成的替代方法,使用染色体等级来避免出现任何健康分布的易感性。进行了实验,以比较简单的基因算法的结果,在单一模式、多式功能和旅行推销员问题(TSP)有限的资源限制范围内,将经常发生突变概率和适应性方法的结果进行比较。对平均最佳健康、几代相演变和全球最佳成就的百分比进行了衡量。结果显示,基于等级的变异变方法优于基于健康的适应方法,在多式联运空间的SGA中也与SGA一样。