A tensor analysis improved genetic algorithm for online bin packing

Asta, Shahriar; �zcan, Ender

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.

Citation

Asta, S., & Özcan, E. A tensor analysis improved genetic algorithm for online bin packing. Presented at Genetic and Evolutionary Computation Conference (2015)

• Conference Name: Genetic and Evolutionary Computation Conference (2015)
• End Date: Jul 15, 2015
• Acceptance Date: Jun 1, 2015
• Publication Date: Jul 11, 2015
• Deposit Date: Jun 14, 2016
• Publicly Available Date: Jun 14, 2016
• Peer Reviewed: Peer Reviewed
• Book Title: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15
• 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
• Public URL: https://nottingham-repository.worktribe.com/output/757220
• Publisher URL: http://dl.acm.org/citation.cfm?doid=2739480.2754787
• Related Public URLs: http://www.sigevo.org/gecco-2015/
• 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
• Contract Date: Jun 14, 2016