Investigating coevolutionary algorithms For expensive fitness evaluations in cybersecurity
Name
1084660520-MIT.pdf
Description
Full printable version
Size
1.8 MB
Format
Adobe PDF
Checksum (MD5)
0e99c95109bdf8ef53404ef2b66257c8
Author(s)
Pertierra Arrojo, Marcos (Marcos A.)
Advisor(s)
Una-May O'Reilly and Erik Hemberg.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Coevolutionary algorithms require evaluating fitness of solutions against adversaries, and vice versa, in order to select high quality individuals to generate offspring and evolve the population. However, some problems require computationally expensive fitness evaluations, which makes it hard to generate solutions in a feasible amount of time. In this thesis, we devise coevolutionary algorithms and methods that achieve good results with fewer fitness evaluations, and we present methods for selecting a solution to deploy after running experiments with multiple coevolutionary algorithms. Comparing our new algorithms presented with baselines, we found that MEULockstepCoev performs relatively well, especially for attackers.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 75-76).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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