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Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture
Joint Authors
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems.
Inspired by evolutionary biology, GA uses selection, crossover, and mutation operators to efficiently traverse the solution search space.
This paper proposes nature inspired fine-tuning to the crossover operator using the untapped idea of Mitochondrial DNA (mtDNA).
mtDNA is a small subset of the overall DNA.
It differentiates itself by inheriting entirely from the female, while the rest of the DNA is inherited equally from both parents.
This unique characteristic of mtDNA can be an effective mechanism to identify members with similar genes and restrict crossover between them.
It can reduce the rate of dilution of diversity and result in delayed convergence.
In addition, we scale the well-known Island Model, where instances of GA are run independently and population members exchanged periodically, to a Continental Model.
In this model, multiple web services are executed with each web service running an island model.
We applied the concept of mtDNA in solving Traveling Salesman Problem and to train Neural Network for function approximation.
Our implementation tests show that leveraging these new concepts of mtDNA and Continental Model results in relative improvement of the optimization quality of GA.
American Psychological Association (APA)
Shrestha, Ajay& Mahmood, Ausif. 2016. Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture. Journal of Mathematics،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108952
Modern Language Association (MLA)
Shrestha, Ajay& Mahmood, Ausif. Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture. Journal of Mathematics No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1108952
American Medical Association (AMA)
Shrestha, Ajay& Mahmood, Ausif. Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture. Journal of Mathematics. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108952
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108952