Research Article
An Improved Function Optimization Problem (IFOP) of Evolutionary Programming Algorithm ñ A Survival Paper
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 39 - Issue 12 |
| Published: February 2012 |
| Authors: R. Karthick, S. Saravanan |
10.5120/4873-7302
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R. Karthick, S. Saravanan . An Improved Function Optimization Problem (IFOP) of Evolutionary Programming Algorithm ñ A Survival Paper. International Journal of Computer Applications. 39, 12 (February 2012), 25-28. DOI=10.5120/4873-7302
@article{ 10.5120/4873-7302,
author = { R. Karthick,S. Saravanan },
title = { An Improved Function Optimization Problem (IFOP) of Evolutionary Programming Algorithm ñ A Survival Paper },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 39 },
number = { 12 },
pages = { 25-28 },
doi = { 10.5120/4873-7302 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A R. Karthick
%A S. Saravanan
%T An Improved Function Optimization Problem (IFOP) of Evolutionary Programming Algorithm ñ A Survival Paper%T
%J International Journal of Computer Applications
%V 39
%N 12
%P 25-28
%R 10.5120/4873-7302
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Evolutionary Algorithms are based on some influential principles like Survival of the Fittest and with some natural phenomena in Genetic Inheritance. The key for searching the solution in improved function optimization problems are based only on Selection and Mutation operators. This paper reflects on Survival selection schemes specifically like Truncate Selection, Proportionate Selection, Tournament Selection and Ranking Based Selection. In this paper we calculate the best fittest value among the populations which is generated.
References
- X.Yao and Y.Xu, “Recent Advances in Evolutionary Computations” Int. J. Compt. Sci and technology
- “Tournament versus Fitness Uniform Selection” by Shane Legg, Marcus Hutter, Akshat Kumar.
- “Natural Computation for Business Intelligence from Web Usage Mining”.by Ajith Abraham
- K-Model “An Evolutionary Algorithm with New Schema of Representation” by Halina Kwasnika.
- Schwefel H.P “On the Evolution of Evolutionary Computation”.
- “Evolutionary Computation” Marc Schoenauer and Zbigniew Michalewicz.
- “Evolving Evolutionary Algorithms for function Optimization” by Mihai Oltean.
- “Evolutionary Algorithms for optimizing bridge deck rehabilitation”
- “Genetic Algorithm” by Tom V.Mathew
- Coercion through optimization: A Classification of Optimization Techniques by sarah wazirud, David C. Brogan and Paul F. Reynolds Jr.
- Y.Carson and A.Maria: “Simulation Optimization: Methods and Applications proceedings of the 1977 Winter Simulation Conference, 1977
- “ Developmental Evaluation in Genetic Programming: A Position Paper” Tuan Hao Hoang, Student Member, IEEE, RI (Bob) McKay, Senior Member, IEEE, Daryl Essam, and Xuan Hoai Nguyen
- Evolutionary Algorithm by “ Zbigniew Michalewiz, Robert Hinterding, Maciej Michalewicz
- “Genetic Solution for Building Design”, by S.Balasubramaniam, C.Kalairaja, S.Karthikeyan, N.Venkateswaran.
- “Selective Mutation for Genetic Algorithm”, Sung Hoon Jung in World Academy of Science, Engineering and Technology 56 2009.
- “Predictive Analytics using Genetic Algorithm for Efficient Supply chain Inventory Optimization”, by P.Radhakrishnan, Dr.V.M. Prasad, N. Jeyanthi.
- “Adaptive Particle Swarm Optimization (APSO) for multimodal function optimization”, Md. Sakhawat Hossen, Fazle Rabbi, Md. Mainur Rahman.
- “Different Aspects of Evolutionary Algorithms, Multi –Objective Optimization Algorithms and Application Domain” , Dhirendra Pal Singh, Ashish Khare.
- “Genetic Algorithm- an Approach to solve Global Optimation”, Prathibha Bajpai and Dr. Manoj Kumar.
- “Penalty Function Methods for Constrained Optimization with Genetic Algorithms” , Ozgur Yeniay. Mathematical and Computational Applications, Vol. 10, No. 1, PP 45-56, 2005
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