Oriented Crossover in Genetic Algorithms for Computer Networks Optimization

Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is propo...

Full description

Saved in:
Bibliographic Details
Published in:Information (Basel) 2023-05, Vol.14 (5), p.276
Main Authors: Rabee, Furkan, Hussain, Zahir M.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Attitudes
Chromosomes
Computer networks
Data mining
Deep learning
Genes
genetic algorithm
Genetic algorithms
Genetic engineering
Genetic research
Mutation
network protocol optimization
Neural networks
Optimization
Optimization techniques
oriented crossover
Parameters
routing algorithm
Simulation methods
uniform crossover
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case where one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and dampen the undesired attitudes. The proposed crossover can be achieved in two stages: The first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system show improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14050276