عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
NATIONAL BIBLIOGRAPHY NUMBER
Number
TL53397
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
A Comparison of the Genetic Algorithm and the Mixing Genetic Algorithm
General Material Designation
[Thesis]
First Statement of Responsibility
Gulfam, Muhammad
Subsequent Statement of Responsibility
Sutton, Andrew
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
University of Minnesota
Date of Publication, Distribution, etc.
2020
GENERAL NOTES
Text of Note
94 p.
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
M.S.
Body granting the degree
University of Minnesota
Text preceding or following the note
2020
SUMMARY OR ABSTRACT
Text of Note
Genetic Algorithms (GAs) are optimization techniques inspired by the idea of evolution. They can sometimes take a long time to find the solution to a problem, but it is not always obvious when, or how to configure their various parameters. Recently, a new GA was introduced [8] that has a lot of potential for parallelization. This algorithm, called the Mixing Genetic Algorithm, has shown promising results on the well-known Traveling Salesman Problem. In this work, we have compared the effectiveness of the Mixing GA over a traditional GA on three discrete optimization problems: the OneMax problem and two topologies of the Ising Model (Ising Model on Tree and Ising Model on Ring). The comparison has been done for the success rate at the given time, for the given problem size and size of population. The comparison has been done for, both, serial and parallel implementations. Overall, the success rate for the Mixing GA is better than the traditional GA. We have also compared two population selection methods, namely, tournament selection and generational population selection. The tournament selection outperformed generational population selection for all the problems and problem sizes that we experimented with.
UNCONTROLLED SUBJECT TERMS
Subject Term
Computer science
PERSONAL NAME - PRIMARY RESPONSIBILITY
Gulfam, Muhammad
PERSONAL NAME - SECONDARY RESPONSIBILITY
Sutton, Andrew
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
University of Minnesota
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب p
[Thesis]
276903
a
Y
