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AdaSort: Adaptive Sorting using Machine Learning

by Somshubra Majumdar, Ishaan Jain, Kunal Kukreja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 12
Year of Publication: 2016
Authors: Somshubra Majumdar, Ishaan Jain, Kunal Kukreja
10.5120/ijca2016910726

Somshubra Majumdar, Ishaan Jain, Kunal Kukreja . AdaSort: Adaptive Sorting using Machine Learning. International Journal of Computer Applications. 145, 12 ( Jul 2016), 12-17. DOI=10.5120/ijca2016910726

@article{ 10.5120/ijca2016910726,
author = { Somshubra Majumdar, Ishaan Jain, Kunal Kukreja },
title = { AdaSort: Adaptive Sorting using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 12 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number12/25328-2016910726/ },
doi = { 10.5120/ijca2016910726 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:35.904956+05:30
%A Somshubra Majumdar
%A Ishaan Jain
%A Kunal Kukreja
%T AdaSort: Adaptive Sorting using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 12
%P 12-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sorting algorithms and their implementations in modern computing requires improvements in sorting large data sets effectively, both with respect to time and memory consumed. This paper is aimed at reviewing multiple adaptive sorting algorithms, on the basis of selection of an algorithm based on the characteristics of the data set. Machine Learning allows us to construct an adaptive algorithm based on the analysis of the experimental data. A review of algorithms designed using Systems of Algorithmic Algebra and Genetic Algorithms was performed. Both methods are designed to target different use cases. Systems of Algorithmic Algebra is a representation of pseudo code that can be converted to high level code using Integrated toolkit for Design and Synthesis of programs, while the Genetic Algorithm attempts to optimize its fitness function and generate the most successful algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Sorting Machine Learning Object oriented programming

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