The following topics are dealt with: genetic regulatory network; cluster analysis; artificial neural networks; gene expression data analysis; support vector machines; microarray classification; RNA; genetic engineerin...
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The following topics are dealt with: genetic regulatory network; cluster analysis; artificial neural networks; gene expression data analysis; support vector machines; microarray classification; RNA; genetic engineering; parallel evolutionary algorithm; and protein family classification.
The papers presented in this special section were presented at the Thirteenth International conference on Intelligent Computing (ICIC) that was held in Liverpool, UK, on August 7-10, 2017. ICIC was formed to provide a...
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The papers presented in this special section were presented at the Thirteenth International conference on Intelligent Computing (ICIC) that was held in Liverpool, UK, on August 7-10, 2017. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computationalbiology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.
The eight papers in this special section were presented at the Sixteenth International conference on Intelligent Computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annua...
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The eight papers in this special section were presented at the Sixteenth International conference on Intelligent Computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computationalbiology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.
This study compares a current representation for evolving networks to model epidemic spread with a novel representation also studied in a companion paper. This study applies a powerful diversity-friendly algorithm cal...
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ISBN:
(纸本)9781665401128
This study compares a current representation for evolving networks to model epidemic spread with a novel representation also studied in a companion paper. This study applies a powerful diversity-friendly algorithm called ring optimization to this novel representation. The problem addressed is that the baseline method is found to optimize only locally;use of the novel representation improves the situation, but not much. The use of ring optimization yields similar or better performance for the ability of the evolved networks to model epidemics while substantially increasing the diversity of those networks.
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