  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.
