Oriented Crossover in Genetic Algorithms for Computer Networks Optimization

Abstract

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.

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

“…These offspring solutions inherit genetic material from their parents in a way that mimics biological reproduction. The goal of crossover is to explore the unseen regions of the search space and produce offspring that inherit the best characteristics of their parents, ideally leading to improved solutions over time [14].…”

“…13.3.200-213 Mobile Crowdsensing (MCS) has emerged as a powerful paradigm for collecting real-world data by leveraging the pervasive presence of mobile devices, such as smartphones and wearables [1]. This innovative approach enables the acquisition of real-time, fine-grained information, making it a critical component in various applications, including smart cities, environmental crossover and mutation [12][13][14]. Game theory, on the other hand, is a mathematical tool for analyzing and modeling strategic interactions among rational decision-makers, often referred to as "players".…”