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Reseach Article

A Rank based Adaptive Mutation in Genetic Algorithm

by Avijit Basak
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 10
Year of Publication: 2020
Authors: Avijit Basak
10.5120/ijca2020920572

Avijit Basak . A Rank based Adaptive Mutation in Genetic Algorithm. International Journal of Computer Applications. 175, 10 ( Aug 2020), 49-55. DOI=10.5120/ijca2020920572

@article{ 10.5120/ijca2020920572,
author = { Avijit Basak },
title = { A Rank based Adaptive Mutation in Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 10 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 49-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number10/31493-2020920572/ },
doi = { 10.5120/ijca2020920572 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:43.370305+05:30
%A Avijit Basak
%T A Rank based Adaptive Mutation in Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 10
%P 49-55
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Adaptive mutation.