Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural network applications in finance: a review and analysis of literature (1990-1996)
Information and Management
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Explorations in LCS Models of Stock Trading
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Applications of Artificial Intelligence in Economics and Finance (Advances in Econometrics)
Applications of Artificial Intelligence in Economics and Finance (Advances in Econometrics)
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Solving a real-world problem using an evolving heuristically driven schedule builder
Evolutionary Computation
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting
ADVCOMP '09 Proceedings of the 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolutionary learning of technical trading rules without data-mining bias
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Using hyperheuristics under a GP framework for financial forecasting
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Financial forecasting is a really important area in computational finance, with numerous works in the literature. This importance can be reflected in the literature by the continuous development of new algorithms. Hyper-heuristics have been successfully used in the past for a number of search and optimization problems, and have shown very promising results. To the best of our knowledge, they have not been used for financial forecasting. In this paper we present pioneer work, where we use different hyper-heuristics frameworks to investigate whether we can improve the performance of a financial forecasting tool called EDDIE 8. EDDIE 8 allows the GP (Genetic Programming) to search in the search space of indicators for solutions, instead of using pre-specified ones; as a result, its search area has dramatically increased and sometimes solutions can be missed due to ineffective search. We apply 14 different low-level heuristics to EDDIE 8, to 30 different datasets, and examine their effect to the algorithm's performance. We then select the most prominent heuristics and combine them into three different hyper-heuristics frameworks. Results show that all three frameworks are competitive, and are able to show significantly improved results, especially in the case of best results. Lastly, analysis on the weights of the heuristics shows that there can be a constant swinging among some of the low-level heuristics, which denotes that the hyper-heuristics frameworks are able to `know' the appropriate time to switch from one heuristic to the other, based on their effectiveness.