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Titlebook: Applications of Evolutionary Computation; EvoApplications 2012 Cecilia Chio,Alexandros Agapitos,Georgios N. Yanna Conference proceedings 20

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51#
發(fā)表于 2025-3-30 08:51:22 | 只看該作者
52#
發(fā)表于 2025-3-30 12:46:57 | 只看該作者
Accompaniment of Business Creationher increases performance. The datasets for evaluation are large intraday forex time series, specifically series from the EUR/USD, the USD/JPY and the EUR/JPY markets, and predictive models for two primary tasks per market are built: intraday return prediction and intraday volatility prediction.
53#
發(fā)表于 2025-3-30 18:49:18 | 只看該作者
Small-World Optimization Applied to Job Scheduling on Grid Environments from a Multi-Objective Persp-II (NSGA-II) to evaluate the multi-objective properties and prove its reliability. Moreover, MOSWO has been compared with real schedulers, the Workload Management System (WMS) from gLite and the Deadline Budget Constraint (DBC) from Nimrod-G, improving their results.
54#
發(fā)表于 2025-3-30 21:07:12 | 只看該作者
55#
發(fā)表于 2025-3-31 01:09:34 | 只看該作者
Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Deritatistical time-series modelling. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets investigated.
56#
發(fā)表于 2025-3-31 07:56:19 | 只看該作者
57#
發(fā)表于 2025-3-31 09:59:31 | 只看該作者
58#
發(fā)表于 2025-3-31 14:06:07 | 只看該作者
59#
發(fā)表于 2025-3-31 18:34:48 | 只看該作者
60#
發(fā)表于 2025-4-1 00:23:06 | 只看該作者
https://doi.org/10.1007/978-3-642-29178-4data mining; genetic algorithm; multi-objective optimization; particle swarm optimization; support vecto
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