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In this paper, one kind of artificial stock market which based on genetic algorithm is built. By using statistic theories and methods, learning behavior of traders in this market is researched. In order to survive in the stock market, traders should learn from each other as new information becoming available and adapt their behavior accordingly over time. It is the interacting of the adaptive traders...
Accurately prediction is the most important way to cost down for airlines. The study was focused on build up forecast model of five Taiwan international flights included Bali, Bang Kong, Ho Chi Minh City, Kuala Lumpur, and Singapore. Genetic programming was adopted to establish simulation models, and Mean Absolute Percent Error (MAPE) also was used to evaluate the performance of those models. The...
This paper proposes an improvement of a recently proposed semantic-based crossover, Semantic Similarity-based Crossover (SSC). The new crossover, called the Most Semantic Similarity-based Crossover (MSSC), is tested with Genetic Programming (GP) on a real world problem, as in predicting the tide in Venice Lagoon, Italy. The results are compared with GP using Standard Crossover (SC) and GP using validation...
The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches,...
Two Computational Intelligence techniques, neural networks-based Multivariate Time Series Model Mining (MVTSMM) and Genetic Programming (GP), have been used to explore the possible relationship between solar activity and temperatures in Central England for the 1721 to 1967 period. Data driven analysis of multivariate, heterogeneous and incomplete time series are used in order to understand the extreme...
Based on the relevance of fractal theory, taking Liaoning Province as an example to research the time series of regional economic growth, applied GP algorithm to calculate the correlation dimension and observe whether it has fractal characteristics, correlation dimension is a relatively simple extraction fractal dimension method through experimental data by calculating fractal dimension, has a certain...
This paper employs Genetic Programming (GP) with individuals of tree structure to form empirical formulas in order to track the dynamic pattern of the moving average curves of stock prices. We find that our method tracks the 60-day moving average better than other shorter period averages. In order to minimize the effects of noise and other random events impacting on the markets and maximize the effective...
The self-similar and nonlinear nature of network traffic makes high accurate prediction difficult. Various technology, including Autoregressive Integrated Moving Average (ARIMA), Local Approximation (LA), Neural Network (NN) etc., have been applied to internet traffic prediction. In this paper, Complex Network based on genetic programming and particle swarm optimization is proposed to predict the...
Change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained. The results...
Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. The application of genetic programming (GP) as a means to automatically generate such trading rules on the stock markets has been studied. Computational results, based on historical pricing and transaction volume data, are reported for the thirty component stocks of the Dow...
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