On the applicability of diploid genetic algorithms in dynamic environments

Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The pres...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2016-09, Vol.20 (9), p.3403-3410
Main Authors: Bhasin, Harsh, Behal, Gitanshu, Aggarwal, Nimish, Saini, Raj Kumar, Choudhary, Shivani
Format: Article
Language:English
Subjects:
Artificial Intelligence
Chromosomes
Computational Intelligence
Control
Engineering
Focus
Genes
Genetic algorithms
Genotype & phenotype
Greedy algorithms
Literature reviews
Mathematical Logic and Foundations
Mechatronics
Mutation
Robotics
Traveling salesman problem
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Summary:Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1803-5