A Rank based Adaptive Mutation in Genetic Algorithm

TL;DR

This paper introduces a novel rank-based adaptive mutation method for genetic algorithms that improves optimization performance over traditional fitness-based adaptive methods, especially in complex multimodal problems.

Contribution

It proposes a new mutation probability scheme using chromosome rank to reduce susceptibility to fitness distribution effects in genetic algorithms.

Findings

Rank-based adaptive mutation outperforms fitness-based adaptive mutation.

The approach achieves higher success in reaching global optima.

It demonstrates improved convergence in multimodal and TSP problems.

Abstract

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.