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首页> 外文会议>IEEE Conference on Computational Intelligence for Financial Engineering Economics >Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm
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Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

机译：结合不同的元启发法以提高财务预测算法的可预测性

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Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.
机译：超启发式方法已成功应用于大量搜索和优化问题。超启发式方法的新颖性之一是它们管理并自动执行元启发式方法的选择过程。在本文中，我们基于模拟退火，禁忌搜索和导引局部搜索这三种元启发式方法实现并分析了一种超启发式框架，该方法已成功应用于过去称为EDDIE的财务预测算法中。 EDDIE使用遗传编程从历史数据中提取和学习，以预测未来的金融市场动向。结果表明，该算法的有效性得到了提高，从而使超启发式框架下的元启发式方法组合成为一种有效的财务预测方法。

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