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Genetic Drift in Genetic Algorithm Selection Schemes [PDF]
IEEE Transactions on Evolutionary Computation, 1999 A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection,
Prügel-Bennett, A., Rogers, A.
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Re-inspiring the genetic algorithm with multi-level selection theory: multi-level selection genetic algorithm [PDF]
Bioinspiration & Biomimetics, 2018 Genetic algorithms are integral to a range of applications. They utilise Darwin's theory of evolution to find optimal solutions in large complex spaces such as engineering, to visualise the design space, artificial intelligence, for pattern classification, and financial modelling, improving predictions.
A J Sobey, P A Grudniewski
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Hybrid genetic algorithms for feature selection [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004 This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared.
Il-Seok Oh, Jin-Seon Lee, Byung-Ro Moon
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A Thermodynamical Selection Rule in the Genetic Algorithm
Transactions of the Institute of Systems, Control and Information Engineers, 1996 The genetic algorithm (GA), an optimization technique based on evolution, suffers often from a phenomenon called the premature convergence. That is, the system often loses the diversity of the population at an early stage of searching. In this paper, the authors propose a novel method called the ThermoDynamical Genetic Algorithm (TDGA), which adopts ...
Naoki Mori +3 more
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, 2011 This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution.
Kishorjit Nongmeikapam +1 more
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Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification [PDF]
Medical and Biological Engineering and Computing, 2021 Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges.
Xiongshi Deng +3 more
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Fast Genetic Algorithm for feature selection — A qualitative approximation approach
Expert systems with applications, 2023 We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets.
Mohammed Ghaith Altarabichi +3 more
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Applied Sciences, 2022 Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance. To
Ismail Damilola Raji +5 more
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A novel community detection based genetic algorithm for feature selection [PDF]
Journal of Big Data, 2020 The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly ...
M. Rostami +2 more
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Mathematics, 2021 Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others.
A. Ewees +8 more
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