Version 1
: Received: 3 November 2016 / Approved: 7 November 2016 / Online: 7 November 2016 (04:57:46 CET)
How to cite:
Eltaeib, T.; Dichter, J. Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm. Preprints2016, 2016110033. https://doi.org/10.20944/preprints201611.0033.v1
Eltaeib, T.; Dichter, J. Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm. Preprints 2016, 2016110033. https://doi.org/10.20944/preprints201611.0033.v1
Eltaeib, T.; Dichter, J. Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm. Preprints2016, 2016110033. https://doi.org/10.20944/preprints201611.0033.v1
APA Style
Eltaeib, T., & Dichter, J. (2016). Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm. Preprints. https://doi.org/10.20944/preprints201611.0033.v1
Chicago/Turabian Style
Eltaeib, T. and Julius Dichter. 2016 "Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm" Preprints. https://doi.org/10.20944/preprints201611.0033.v1
Abstract
This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.