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• W2000254571 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." @default.
• W2000254571 created "2016-06-24" @default.
• W2000254571 creator A5003488448 @default.
• W2000254571 creator A5020026827 @default.
• W2000254571 date "2015-07-11" @default.
• W2000254571 modified "2023-10-17" @default.
• W2000254571 title "A Tensor Analysis Improved Genetic Algorithm for Online Bin Packing" @default.
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• W2000254571 doi "https://doi.org/10.1145/2739480.2754787" @default.
• W2000254571 hasPublicationYear "2015" @default.
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