A tensor analysis improved genetic algorithm for online bin packing

Asta, Shahriar and Özcan, Ender (2015) A tensor analysis improved genetic algorithm for online bin packing. In: Genetic and Evolutionary Computation Conference (2015), 11-15 July 2015, Madrid, Spain.

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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.

Item Type: Conference or Workshop Item (Paper)
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
Keywords: Genetic Algorithm, Bin Packing Problem, Tensor, Genetic Diversity, Fixation (Popular Genetics), Natural Selection, Locus (Genetics), Mutation
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1145/2739480.2754787
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Depositing User: Ozcan, Dr Ender
Date Deposited: 14 Jun 2016 08:35
Last Modified: 20 Sep 2016 10:55
URI: http://eprints.nottingham.ac.uk/id/eprint/33940

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