Abid Hussain

Work place: Department of Statistics, Quaid-i-Azam University, 44000 Islamabad, Pakistan

E-mail: abid0100@gmail.com


Research Interests: Computational Mathematics, Computer systems and computational processes, Combinatorial Optimization


Dr. Abid Hussain did his Ph.D. (Statistics) in 2018. His areas of interest are Operations Research, Stochastic Processes, Optimization Theories and Computational Mathematics.

Author Articles
Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

By Ehtasham-ul-Haq Abid Hussain Ishfaq Ahmad

DOI: https://doi.org/10.5815/ijisa.2019.12.05, Pub. Date: 8 Dec. 2019

This research work provides a detailed working principle and analysis technique of multi- offspring crossover operator. The proposed approach is an extension of the basic partially- mapped crossover (PMX) based upon survival of the fittest theory. It improves the performance of the genetic algorithm (GA) for solving the well-known combinatorial optimization problem, the traveling salesman problem (TSP). This study is based on numerical experiments of the proposed with other traditional crossover operators for eighteen benchmarks TSPLIB instances. The simulation results show a considerable improvement because the proposed operator enhances the opportunity of having better offspring. Moreover, the t-test also establishes the improved significance of the proposed operator. Its preferable results not only confirm the advantages over others, but also show the long run survival of a generation having a number of offspring more than the number of parents with the help of mathematical ecology theory.

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An Efficient Genetic Algorithm for Numerical Function Optimization with Two New Crossover Operators

By Abid Hussain Yousaf Shad Muhammad Muhammad Nauman Sajid

DOI: https://doi.org/10.5815/ijmsc.2018.04.04, Pub. Date: 8 Nov. 2018

Selection criteria, crossover and mutation are three main operators of genetic algorithm’s performance. A lot of work has been done on these operators, but the crossover operator has a vital role in the operation of genetic algorithms. In literature, multiple crossover operators already exist with varying impact on the final results. In this article, we propose two new crossover operators for the genetic algorithms. One of them is based on the natural concept of crossover i.e. the upcoming offspring takes one bit from a parent and next from other parent and continuously takes bits till last one. The other proposed scheme is the extension of two-point crossover with the concept of multiplication rule. These operators are applied for eight benchmark problems in parallel with some traditional crossover operators. Empirical studies show a remarkable performance of the proposed crossover operators.

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