Skip to main content

Research Repository

Advanced Search

A tensor analysis improved genetic algorithm for online bin packing

Asta, Shahriar; Özcan, Ender

Authors

Shahriar Asta



Abstract

Mutation in a Genetic Algorithm is the key variation operator adjusting the genetic diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value of a gene. In this study, we describe a novel data science approach to adaptively generate the mutation probability for each locus. The trail of high quality candidate solutions obtained during the search process is represented as a 3rd order tensor. Factorizing that tensor captures the common pattern between those solutions, identifying the degree of mutation which is likely to yield improvement at each locus. An online bin packing problem is used as an initial case study to investigate the proposed approach for generating locus dependent mutation probabilities. The empirical results show that the tensor approach improves the performance of a standard Genetic Algorithm on almost all classes of instances, significantly.

Publication Date Jul 11, 2015
Peer Reviewed Peer Reviewed
Book Title Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15
APA6 Citation Asta, S., & Özcan, E. (2015). A tensor analysis improved genetic algorithm for online bin packing. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15. https://doi.org/10.1145/2739480.2754787
DOI https://doi.org/10.1145/2739480.2754787
Keywords Genetic Algorithm, Bin Packing Problem, Tensor, Genetic Diversity, Fixation (Popular Genetics), Natural Selection, Locus (Genetics), Mutation
Publisher URL http://dl.acm.org/citation.cfm?doid=2739480.2754787
Related Public URLs http://www.sigevo.org/gecco-2015/
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
Additional Information Published in: GECCO '15 : proceedings and companion publication of the 2015 Genetic and Evolutionary Conference : July 11-15, 2015, Madrid, Spain. New York : ACM, 2015, ISBN: 978-1-4503-3472-3. pp. 799-806, doi:10.1145/2739480.2754787

Files

gecco2015.pdf (296 Kb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





You might also like



Downloadable Citations

;