Submitted:
03 November 2016
Posted:
07 November 2016
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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.
Keywords:
genetic algorithms
; parallel computation
; computational complexity
; algorithms
; optimization techniques
; traveling salesman problem
; NP-Hard problems
; Berlin-52 data set
; machine learning
; linear regression
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