We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Optimization of Dejong Function using GA under Different Selection Algorithms

by Kapil Juneja, Nasib Singh Gill
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 7
Year of Publication: 2013
Authors: Kapil Juneja, Nasib Singh Gill
10.5120/10648-5407

Kapil Juneja, Nasib Singh Gill . Optimization of Dejong Function using GA under Different Selection Algorithms. International Journal of Computer Applications. 64, 7 ( February 2013), 28-33. DOI=10.5120/10648-5407

@article{ 10.5120/10648-5407,
author = { Kapil Juneja, Nasib Singh Gill },
title = { Optimization of Dejong Function using GA under Different Selection Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 7 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number7/10648-5407/ },
doi = { 10.5120/10648-5407 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:47.236760+05:30
%A Kapil Juneja
%A Nasib Singh Gill
%T Optimization of Dejong Function using GA under Different Selection Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 7
%P 28-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are numbered of Numerical and Mathematical Problem that takes the exponential time to process called NP Problems. These kinds of problems always need some optimization algorithm to complete the process in effective time. Genetic is one of the adaptive evolutionary algorithmic approaches to solve such NP problems in optimize time frame. In this work, A Genetic approach is defined to optimize the solution of Rosenbrock's valley problem. The work also includes the Optimization analysis of different parametric changes in selection and crossover phase. In this paper, the result of the diverse models is presented in the form of the graph.

References
  1. Victor M. Kureichick, "Genetic Algorithm for Solution of the Traveling Salesman Problem with New Features against Premature Convergence", Working Paper, 1996.
  2. Rajeev Kumar, "Evolution of Hyperheuristics for the Biobjective 0/1 Knapsack Problem by Multiobjective Genetic Programming", GECCO'08, July 12–16, 2008, Atlanta, Georgia, USA, pp 1227-1234
  3. Christian Horoba, "Ant Colony Optimization for Stochastic Shortest Path Problems", GECCO'10, July 7–11, 2010, Portland, Oregon, USA, pp 1465-1475
  4. Dervis KARABOGA," A Simple and Global Optimization Algorithm for Engineering Problems: Di_erential Evolution Algorithm", Turk J ElecEngin, VOL. 12, NO. 1 2004, cT¨ UB_ ITAK
  5. JORGE J. MORE, "Benchmarking derivative-freeoptimization algorithms". SIAM J. Optimization, Vol. 20 (1), pp. 172-191, 2009
  6. A. Rangel-Merino," Optimization Method based on Genetic Algorithms". Apeiron, Vol. 12, No. 4, October 2005
  7. Martin Pelikan," BOA: The Bayesian Optimization Algorithm", Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), I, pp 525-532
  8. Gerhard Venter," A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations", 6th World Congresses of Structural and Multidisciplinary Optimization, 2005
  9. Zhouchen lin,"Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix", In Intl. Workshop on Comp. Adv. in Multi-Sensor Adapt. Processing Aruba, 2008
  10. Gulshan Singh," Comparison of Multi-Modal Optimization Algorithms Based on Evolutionary Algorithms". GECCO '06 Proceedings of the 8th annual conference on Genetic and evolutionary computation, Pages 1305-1312
  11. Marcin Molga, "Test functions for optimization needs", 3 kwietnia 2005
  12. Jeffrey W. Heath," New Global Optimization Algorithms for Model-Based Clustering". Journal of Computational and Graphical Statistics. 2009
  13. M. A. Panduro,"A comparison of genetic algorithms, parti- cle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays", Progress In Electromagnetics Research 2008
Index Terms

Computer Science
Information Sciences

Keywords

DeJong Function Genetics Optimization Selection Crossover