Papers by Keyword: Gene Expression Programming

Abstract: Limestone and slag blended concrete is an innovative concrete which belongs to the family of limestone calcined clay cement (LC3) concrete. Strength is an important property of structural concrete. This study shows artificial neural networks (ANN) and gene expression programming (GEP) models for predicting strength development of limestone and slag blended concrete. ANN model consists of an input layer, a hidden layer, and output layer. GEP model consists of the sum of three expression trees. The input parameters of ANN and GEP models are mixtures and ages. The output parameter is a strength. The correlation coefficients of ANN and GEP model are 0.99 and 0.98, respectively. Both ANN and GEP model can produce prediction results of the strength of ternary blended concrete reliably.

Abstract: Traditional algorithm for mining association rules need to scan the database many times when mining association rules, which are inefficient and time-wasting. In the light of the defects of traditional algorithm, this paper introduces the improvement of community partition algorithm into the process of association mining and uses the Internet forum users’ data of a university as the object of the new algorithm for mining association rules. The experiment shows that the new algorithm can help optimize the data association rules mining and reduces the times and time of scanning the database, so the mining efficiency is greatly enhanced.

Abstract: The coffee flavor compounds acquire a significant place in the improving the flavor of cigarette. In the present paper, the gene expression programming (GEP) is used to develop quantitative relationships between the retention time (TR) and four molecular descriptors of 52 compounds. The model of GEP gives good statistical result. This method can be used to set up the good regression model.

Abstract: Digital design has resulted in the need for a reconfigurable of current design theories. The present research postulates the oretical framework of informed tectonics in digital design. The research for the evolution algorithmgene expression programming is developed and discussed. This paper mainly talk about the oretical framework of informed tectonics in digital design based on evolution algorithmGEP. The methodologies is used to explain and guide future research anddevelopment.

Abstract: In order to study a mine rescue robot model, gene expression programming algorithm is studied. The gene expression programming Algorithm can simulate many scientific models, and has been successfully applied in many aspects. Particle swarm optimization algorithm is discussed. Each member of the particle swarm optimization group can study its own experience and other members' experience to continuously change their search mode. Finally, a coal mine rescue robot model based on the gene expression programming and particle swarm optimization is put forward.

Abstract: To improve model accuracy,tabu search is introduced to Gene Expression Programming (GEP) and impoves GEPs local search ability, Gene Expression Programming Based on Parallel Tabu Search (PTS-GEP) is proposed. In PTS-GEP, the research conducts experiment over the data from previously reported research and compares the result to two other algorithms namely simple GEP, UC-GEP. The results demonstrate the optimal performance of PTS-GEP in model accuracy.

Abstract: This paper introduces a method which uses the gene expression programming algorithm to conduct multivariate nonlinear function modeling, which is applied in the earthquake magnitude prediction. The experiment shows that the prediction accuracy of the GEP is significantly higher than that of the neural network model. Finally, by using the non-delayed effects and stability of the earthquake magnitude prediction data, the state-transition matrix is obtained through the Markov chain, and the state interval and corresponding probability of the GEP model prediction are obtained. In this way, the credibility of the prediction results has been increased.

Abstract: According to the situation of frequently domestic mining safety accidents, the basic theory and related concepts of bioinformatics' gene expression programming and multi-agent system are discussed. Related concepts of Bioinformatics and biological evolution and evolutionary computation are described in this paper. A coal mine rescue robot working model is discussed based on bioinformatics gene expression programming algorithm and multi-agent system theory.

Abstract: The forecasting technique is one of the key factors for load forecasting. According to analysis, the Fast Intrinsic Mode Decomposition (FIMD) method is applied to short-term load forecasting in this paper. The specific implementation process of the proposed short-term load forecasting method is in the following. The selected load sample data are decomposed into a number of stationary Intrinsic Mode Functions (IMFs) with respective single mode by the FIMD method firstly. Then, each obtained load component with different frequency band is forecasted according to the Gene Expression Programming (GEP) method by time-sharing. The final forecasting models are obtained by rebuilding the forecasting model of each IMF. Lots of virtual forecasting tests are done, and it proves that the proposed load forecasting method based on the FIMD method in this paper is more accurate than the method based on Empirical Mode Decomposition (EMD).

Abstract: To further solve the expansion performance problems of collaborative filtering technology. Firstly, form a few better center areas by using density partition and numerical distance between objects. Then, use the gene expression programming（GEP） to find the cluster centers. Propose a cluster algorithm of Density-based methods GEP-Cluster (DGEPC) to solve the nearest neighbor problem. Push the GEP technology for the Personalization Recommendation system.