The week's pick
Reseach Article
Crossover Operators in Genetic Algorithms: A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 162 - Number 10 |
| Year of Publication: 2017 |
| Authors: Padmavathi Kora, Priyanka Yadlapalli |
10.5120/ijca2017913370
|
Padmavathi Kora, Priyanka Yadlapalli . Crossover Operators in Genetic Algorithms: A Review. International Journal of Computer Applications. 162, 10 ( Mar 2017), 34-36. DOI=10.5120/ijca2017913370
Abstract
Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Genetic algorithms are very effective way of finding a very effective way of quickly finding a reasonable solution to a complex problem. Performance of genetic algorithms mainly depends on type of genetic operators which involve crossover and mutation operators. Different crossover and mutation operators exist to solve the problem that involves large population size. Example of such a problem is travelling sales man problem, which is having a large set of solution. In this paper we will discuss different crossover operators that help in solving the problem.
References
- Ambika Annavarapu, Padmavathi Kora, "ECG-based Atrial Fibrillation detection using different orderings of Conjugate Symmetric–Complex Hadamard Transform," International Journal of Cardiovascular Academy, Elsevier, Aug 2016.
- C. Ryan, Automatic re-engineering of software using genetic programming, Genetic Programming Series. Kluwer Academic Publishers, ISBN 0-7923-8653-1, 2000.
- Chiung Moon, Jongsoo Kim, GyunghyunChoi ,YoonhoSeo,” An efficient genetic algorithm for the traveling salesman problem with precedence constraints”, European Journal of Operational Research 140 (2002) 606–617, accepted 28 February 2001
- D.E. Goldberg, Genetic algorithms in search, optimisation, and machine learning, Addison Wesley Longman, Inc., ISBN 0-201-15767-5, 1989
- Ivan Brezina Jr.,ZuzanaCickova, “Solving the Travelling Salesman Problem using the Ant colony Optimization”, Management Information Systems, 2011, Vol. (6), No. (4).
- J. Holland, Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, 1975.
- Manju Sharma , Novel Knowledge based Selective Tabu Initialization in Genetic algorithm, IJARCSSE, Volume 3, Issue 5, May 2013
- Padmavathi Kora, K. Sri Rama Krishna, “Bundle Block Detection using Genetic Neural Network,” International System design and Intelligent Applications (INDIA), AISC, Springer, Jan 2016.
- Padmavathi Kora, and Sri Rama Krishna, “Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block,” SpringerPlus, Springer, vol 4, no 1, 481, Sep 2015.
- Padmavathi Kora, and Sri Ramakrishna Kalva, ”Improved Bat algorithm for the detection of myocardial infarction,” SpringerPlus, Springer, vol 4, no. 1, pp. 1-18, Nov 2015.
- Rong Yang, “Solving Large Travelling Salesman Problems with Small Populations”. IEEE 1997.
- ShubhraSankar Ray, Sanghamitra Bandyopadhyay and Sankar K.Pal,” New Operators of Genetic Algorithms for Traveling Salesman Problem”, 2000IEEE.
- W. E. Hart, “Adaptive global optimization with local search, Doctoral diss.”, San Diego, University of California, 1994.
Index Terms
Keywords