Comparative analysis of genetic crossover operators in knapsack problem

The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Applied Sciences and Environmental Management 2016-09, Vol.20 (3), p.593-593
Main Authors: Hakimi, D, Oyewola, D.O., Yahaya, Y, Bolarin, G
Format: Article
Language:English
Subjects:
Arithmetic
Branch & bound algorithms
Chromosomes
Crossover
Evolutionary Algorithm
Genetic Algorithm
Genetic algorithms
Heuristic
Intermediate
Parents & parenting
Problems
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.
ISSN:1119-8362
2659-1502
1119-8362
2659-1499
DOI:10.4314/jasem.v20i3.13