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In order to increase the accuracy of abnormal event detection in crowd video surveillance, this paper proposes a novel hybrid optimization of feature selection and support vector machine (SVM) training model based on genetic algorithm. For reducing dimensions of multi-feature, we propose an adaptive genetic simulated annealing algorithm (ASAGA) feature selection method. The ASAGA takes advantage of...
In this paper we train an Artificial Neural Network (ANN) using Memetic Algorithm (MA) and evaluate its performance on the UCI spambase dataset. The Memetic algorithm incorporates the local search capacity of Simulated Annealing (SA) and the global search capability of Genetic Algorithm (GA) to optimize the parameters of the ANN. The performance of the MA is compared with traditional GA in training...
The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the expertâs knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments. In this paper...
A meme in the context of optimization represents a unit of algorithmic abstraction that dictates how solution search is carried out. At a higher level, a meta-meme serves as an encapsulation of the scheme of interplay between memes involved in the search process. This paper puts forth the notion of neural meta-memes to extend the collective capacity of memes in problem-solving. We term this as Neural...
In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART classifiers to optimize both their classification error and their structural complexity. Towards this end, it combines the use of interacting sub-populations, some traditional elements of genetic algorithms to evolve these...
Price index forecasting is one of the most important problems in financial markets. In the past decades the prediction of stock index has played a vital role in the financial situation of several companies which have stocks in the market. In this paper we use Multi Layer Perceptron (MLP) neural network in stock index prediction. Three searching algorithms were used to get the best network architecture...
In pursuit of better CRTh2 receptor antagonist agents, 2D-QSAR, 3D- QSAR studies were performed on a series of 2,4-disubstituted-phenoxy acetic acid derivatives. The best QSAR model was selected, having correlation coefficient R = 0.904, standard error of estimation SEE = 0.456 and cross validated squared correlation coefficient Q2 = 0.739. The predictive ability of the selected model was also confirmed...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local minimum, the genetic algorithm and simulated annealing algorithm with the overall search capability have been put forward to optimize authority value and threshold value of BP nerve network. In this paper, a new neural network model which is optimized by genetic algorithm...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local infinitesimal, the genetic algorithm and simulated annealing algorithm with the overall search capability has been put forward to optimize authority value and threshold value of BP nerve network. In this paper, GA-SA neural network algorithm model has been established...
Text classification has received extensive attention in recent years, which is an important means of data mining. This paper analyzed basic theory and general structure of text classification, given a text classification method based on improved genetic algorithms, introduced simulated annealing mechanism of genetic algorithm to solve the precocious easy, local optimum, and so on, using the Roocchio...
A support vector machines method (SVM) is presented for the hourly load forecasting of the coming days. In this approach, improved SVM based on simulated annealing algorithm and genetic algorithm. The new approach is used for the next day load forecast. These two methods are combined to find the improved parameters for Support Vector Machine. It proves that the combined method is useful in improve...
This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of multi-layer perceptron neural networks. We compare the performance...
Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. This paper studies the application of a variety of tuning techniques for optimizing the least squares support vector machines (LS-SVM) hyper-parameters in a short-term load forecasting problem. Clearly, the construction of any effective and accurate LS-SVM model depends on carefully...
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