The Plymouth Student Scientist
Document Type
Engineering, Computing and Mathematics Article
Abstract
The art of using evolutionary mechanisms for identifying satisfiability has produced a range of efficient solutions to this otherwise computationally challenging problem. Since their first use these evolutionary methods have been changed and adapted to produce increasingly efficient solutions. This paper introduces two unique alternatives to the optimisation of these methods, the first through the introduction of alternative mutation operators and the second through utilizing a grammatical encoding which has been proven to improve neuroevolution. The goal of this paper is to identify whether these two alternatives are candidates for future investigation in improving evolutionary satisfiability solvers.
Publication Date
2017-12-01
Publication Title
The Plymouth Student Scientist
Volume
10
Issue
2
First Page
193
Last Page
207
ISSN
1754-2383
Deposit Date
May 2019
Embargo Period
2024-07-08
URI
http://hdl.handle.net/10026.1/14165
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Barnes, Andrew
(2017)
"Genetic optimisations for satisfiability and Ramsey theory,"
The Plymouth Student Scientist: Vol. 10:
Iss.
2, Article 2.
Available at:
https://pearl.plymouth.ac.uk/tpss/vol10/iss2/2
