Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator
Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the travel...
Main Authors: | Abid Hussain, Yousaf Shad Muhammad, M. Nauman Sajid, Ijaz Hussain, Alaa Mohamd Shoukry, Showkat Gani |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2017-01-01
|
Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2017/7430125 |
Similar Items
-
Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach.
by: Yousaf Shad Muhammad, et al.
Published: (2016-01-01) -
A new multi-offspring crossover operator for genetic algorithm to facilitate the traveling salesman problem
by: Abid Hussain, et al.
Published: (2019-12-01) -
Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection
by: Yousaf Shad Muhammad, et al.
Published: (2019-01-01) -
An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator
by: Md. Sabir Hossain, et al.
Published: (2019-12-01) -
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
by: Zulifqar Ali, et al.
Published: (2017-01-01)