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We propose a new method to evaluate individuals in genetic algorithms (GAs) for algorithmic trading in stock markets. In our previous work, we presented an effective method to acquire trading strategy in stock markets. However, it had a tendency of overfitting in genetic searches. Our new approach, namely neighborhood evaluation, involves evaluation for neighboring points of genetic individuals in...
This paper presents a stereo matching algorithm utilizing vertical disparity (SMAVD) in solving the matching problem of stereo vision. SMAVD adopts a two-dimensional Hopfield neural network (HNN) to match the stereo pairs according to the energy function developed to describe three constraints including uniqueness, similarity and compatibility. The similarity of one matched pair is measured according...
This paper proposes a novel hybrid algorithm to determine the parameters (number of neurons, centers, widths and weights) of radial basis function neural networks automatically. In this work, a hybrid algorithm combines the multi-encoding genetic algorithm (MGA) and the back propagation (BP) algorithm to form a hybrid learning algorithm (MGA-BP) for training radial basis function networks (RBFNs),...
The main purpose of this paper is to propose an incorporating a grammatical evolution (GE) into the genetic algorithm (GA), called GEGA, and apply it to estimate the compressive strength of high-performance concrete (HPC). The GE, an evolutionary programming type system, automatically discovers complex relationships between significant factors and the strength of HPC in a more transparent way to enhance...
Combining genetic algorithms and artificial neural networks, a hybrid genetic-neural method was proposed for modeling the nonlinear dynamic deformation system considering the effect of environmental factors. This method describes the characteristic of nonlinear evolvement of deformation using ANN (the artificial neural network) whose structure (including nodes of input layer and hide layer) is automatically...
Software cost estimation affects almost all activities of software project development such as: bidding, planning, and budgeting, thus it is very crucial to the success of software project management. In past decades, many methods have been proposed for cost estimation. Analogy based cost estimation (ABE) is among the most popular techniques due to its conceptual simplicity and empirical competitiveness...
It is a very important premise of nerve slice images registration to recognize the positioning line transection in the image. On the basis of the feature analyzing of positioning line transection, this paper designs the training and testing module sets. Then the least square support vector machine (LS-SVM) is trained by the training module set and optimized by genetic algorithm (GA) according to the...
This paper proposes the use of genetic algorithm to select an optimal feature set for distinguishing computer graphics from digital photographic images. Our previously developed approach has derived a 234-D feature vector from each test image in HSV color space. The statistical moments of characteristic functions of the image and its wavelet subbands were selected as the distinguishing image features...
The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relearning...
SVM performance is very sensitive to the parameter set. In this paper we propose an automatic and effective model selection method. It is based on evolutionary computation algorithms and use recall, precision and error rate estimated by xialpha-estimate as the optimization targets. Optimized by genetic algorithm (GA) or particle swarm optimization (PSO) algorithm, we demonstrate that SVM could automatically...
Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We propose a two-step approach that first uses genetic algorithms to reduce the number of features used to...
This paper presents a hybrid TSK fuzzy rule-based classifier. Fuzzy c-means clustering and genetic algorithm and are used to optimize the number of rules and antecedent parameters. By using the relationship between a SVM and a TSK FLS, an efficient method for learning the consequent parts of the TSK fuzzy system is introduced. The resulting hybrid fuzzy classifier has a compact rule base and good...
Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameters of sewage treatment quality can not be detected on-line, a soft measurement modeling method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic...
Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation modeling method based on high speed and precise genetic algorithm neural network is presented. The high speed and precise genetic algorithm neural network...
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