象形文字
发表于 2025-3-26 21:48:55
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ensemble
发表于 2025-3-27 01:34:35
Adaptive Real-Time Dynamic Programmingse from a canonical genetic algorithm. Furthermore, we apply QIEA to a finance problem, namely non-linear principal component analysis of implied volatilities. The results from the algorithm are shown to be robust and they suggest potential for useful application of the QIEA to high-dimensional optimization problems in finance.
无脊椎
发表于 2025-3-27 06:35:37
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vertebrate
发表于 2025-3-27 11:55:04
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大吃大喝
发表于 2025-3-27 17:40:38
Evolutionary System for Generating Investment Strategieslutionary algorithm, co-evolutionary algorithm, and agent-based co-evolutionary algorithm) are verified and compared on the basis of the results coming from experiments carried out with the use of real-life stock data.
正式演说
发表于 2025-3-27 19:45:11
Horizontal Generalization Properties of Fuzzy Rule-Based Trading Modelsas inputs and produce a trading signal for day . + 1 based on a dataset of past observations of which actions would have been most profitable..The approach has been applied to trading several financial instruments (large-cap stocks and indices), in order to study the ., i.e., cross-market, generalization capabilities of the models.
instulate
发表于 2025-3-28 01:05:25
Option Model Calibration Using a Bacterial Foraging Optimization Algorithm is then used for calibration of a volatility option pricing model. The results from the algorithm are shown to be robust and extendable, suggesting the potential of applying the BFO for financial modeling.
HALL
发表于 2025-3-28 04:27:47
Quantum-Inspired Evolutionary Algorithms for Financial Data Analysisse from a canonical genetic algorithm. Furthermore, we apply QIEA to a finance problem, namely non-linear principal component analysis of implied volatilities. The results from the algorithm are shown to be robust and they suggest potential for useful application of the QIEA to high-dimensional optimization problems in finance.
Intact
发表于 2025-3-28 07:32:59
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unstable-angina
发表于 2025-3-28 11:10:45
Encyclopedia of Machine Learningoblem through the Kuhn–Tucker approach. The proposed technique does not require any gradient information but cost function values solely, while a penalty function is employed to address the cases of limited warehouse capacity. Experiments are conducted on models proposed in the relative literature, justifying the usefulness of the algorithm.