Enhancing genetic algorithms using multi mutations
- Published
- Accepted
- Subject Areas
- Algorithms and Analysis of Algorithms, Artificial Intelligence
- Keywords
- evolutionary algorithms, Genetic algorithms, Multi Mutations, TSP
- Copyright
- © 2016 Hassanat et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Enhancing genetic algorithms using multi mutations. PeerJ Preprints 4:e2187v1 https://doi.org/10.7287/peerj.preprints.2187v1
Abstract
Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm’s performance, particularly when using more than one mutation operator.
Author Comment
This is a submission to PeerJ Computer Science for review.