Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2008: E Mario Giacobini,Anthony Brabazon,Shengxiang Yang Conference proceedings 2008 S

象形文字

ensemble

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.

無(wú)脊椎

vertebrate

大吃大喝

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.

正式演說(shuō)

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

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

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

unstable-angina

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.