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Titlebook: Applications of Evolutionary Computation; 20th European Confer Giovanni Squillero,Kevin Sim Conference proceedings 2017 Springer Internatio

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發(fā)表于 2025-3-28 14:35:39 | 只看該作者
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發(fā)表于 2025-3-28 19:01:05 | 只看該作者
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發(fā)表于 2025-3-28 23:09:46 | 只看該作者
Pricing Rainfall Based Futures Using Genetic Programming(BA) across contracts available on the CME. Our goal is twofold, (i) to show that by improving the predictive accuracy of the rainfall process, the accuracy of pricing also increases. (ii) contract-specific models can further improve the pricing accuracy. Results show that both of the above goals ar
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發(fā)表于 2025-3-29 06:06:07 | 只看該作者
De Novo DNA Assembly with a Genetic Algorithm Finds Accurate Genomes Even with Suboptimal Fitnessld in over 95% of all assembly runs. For the smaller read sets, the scaffold obtained consists of only the correct contig; for the larger read libraries, the fitness of the solution is suboptimal, with chaff contigs present; however, a simple post-processing step can realign the chaff onto the corre
45#
發(fā)表于 2025-3-29 07:37:09 | 只看該作者
EVE: Cloud-Based Annotation of Human Genetic Variantsression Atlas, miRNA annotations, minor allele frequencies from the 1000 Genomes Project and the Exome Aggregation Consortium, and deleteriousness scores from Combined Annotation Dependent Depletion. We demonstrate the utility of EVE by annotating the genetic variants in a case-control study of glau
46#
發(fā)表于 2025-3-29 12:22:31 | 只看該作者
Objective Assessment of Cognitive Impairment in Parkinson’s Disease Using Evolutionary AlgorithmFrom the experimental data, 25 kinematic features were extracted with the aim of generating a classifier that is able to discriminate not only between Controls and PD patients, but also between the PD cognitive subgroups. The technique used to find the best classifier was an Evolutionary Algorithm -
47#
發(fā)表于 2025-3-29 17:36:55 | 只看該作者
48#
發(fā)表于 2025-3-29 21:24:48 | 只看該作者
Enhancing Grammatical Evolution Through Data Augmentation: Application to Blood Glucose Forecastingoduce several GE models that work together in a combining system. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using data augmentation.
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發(fā)表于 2025-3-30 02:33:14 | 只看該作者
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發(fā)表于 2025-3-30 05:42:20 | 只看該作者
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