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Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on...
Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search...
In this paper, we implement a new method for classification of biological signals in general, and use it in the animal behavior classification as an example. The forced swimming test of rats or mice is a frequently used behavioral test to evaluate the efficacy of drugs in rats or mice. Frequently used features for that evaluation are obtained through observing three states: immobility, struggling/climbing...
In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs)...
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...
Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search...
This paper describes an improved version of a method that automatically searches near-optimal multi-layer feedforward artificial neural networks using genetic algorithms. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can produce compact, efficient...
In this paper, we propose a novel Q-learning algorithm that segmentalizes the agent environment and action. This algorithm is learned through interaction with an environment and provides deterministic space segmentation. The purposes of this study can be divided into two main groups: search domain reduction and heuristic space segmentation. In our method, the most activated space segment is divided...
This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robust neural network training purpose. Conventional neural network training algorithms based on the gradient descent often encounter local minima problems. Recently, some evolutionary algorithms are getting a lot more attention about global search ability but are less-accurate for complicated...
This work focuses on a study about hybrid optimization techniques for improving feature selection and weighting applications. For this purpose, two global optimization methods were used: Tabu search (TS) and simulated annealing (SA). These methods were combined to k-nearest neighbor (k-NN) composing two hybrid approaches: SA/k-NN and TS/k-NN. Those approaches try to use the main advantage from the...
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored...
In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing...
Job-shop scheduling problem is a typical representative of NP-complete problems and it is also a popular topic for the researchers during the recent decades. Lots of artificial intelligence techniques were used to solve this kind of problems, such as: genetic algorithm, tabu searching method, simulated annealing and neural network. Based on the previous research of Zhou and Willems, this paper proposes...
Currently many non-tractable considered problems have been solved satisfactorily through methods of approximate optimization called metaheuristic. These methods use non-deterministic approaches that find good solutions which, however, do not guarantee the determination of the global optimum. The success of a metaheuristic is conditioned by capacity to adequately alternate between exploration and exploitation...
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