A Rank based Adaptive Mutation in Genetic Algorithm
This is an incremental improvement for researchers using genetic algorithms in optimization problems, particularly for multimodal functions.
The paper tackles the problem of susceptibility to fitness distribution in adaptive mutation probabilities for genetic algorithms by proposing a rank-based approach instead of fitness-based. Results show this method outperforms both simple genetic algorithms and fitness-based adaptive approaches in multimodal problem spaces, with measurements including average best fitness, generations evolved, and percentage of global optimum achievements.
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.