Extreme learning machine (ELM) is an emergent method for train- ing single hidden layer feedforward neural networks (SLFNs) with extremely fast training speed, easy implementation and good generalization performance. This work presents effective ensemble procedures for combining ELMs by exploiting di- versity. A large number of ELMs are initially trained in three different scenarios: the original feature input space, the obtained feature subset by forward selection and different random subsets of features. The best combination of ELMs is con- structed according to an exact ranking of the trained models and the useless net- works are discarded. The experimental results on several regression problems show that robust ensemble approaches that exploit diversity can effectively improve the performance compared with the standard ELM algorithm and other recent ELM extensions.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization.The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum.And the initial phase of the breeding process starts with parent selection.The selection utilized in a GA is effective on the convergence speed of the algorithm.A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals.Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study.And the use of neural networks aims to produce better offspring during the GA search.The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring.In the breeding process, the first parent is selected based on the fitness value as usual.Then it is the neural network that decides the appropriate mate for the first parent chosen.Hence, the selection mechanism is not solely dependent on the fitness values in this study.The algorithm is tested with seven benchmark functions.It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization.The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum.And the initial phase of the breeding process starts with parent selection.The selection utilized in a GA is effective on the convergence speed of the algorithm.A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals.Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study.And the use of neural networks aims to produce better offspring during the GA search.The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring.In the breeding process, the first parent is selected based on the fitness value as usual.Then it is the neural network that decides the appropriate mate for the first parent chosen.Hence, the selection mechanism is not solely dependent on the fitness values in this study.The algorithm is tested with seven benchmark functions.It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization.The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum.And the initial phase of the breeding process starts with parent selection.The selection utilized in a GA is effective on the convergence speed of the algorithm.A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals.Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study.And the use of neural networks aims to produce better offspring during the GA search.The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring.In the breeding process, the first parent is selected based on the fitness value as usual.Then it is the neural network that decides the appropriate mate for the first parent chosen.Hence, the selection mechanism is not solely dependent on the fitness values in this study.The algorithm is tested with seven benchmark functions.It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization.The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum.And the initial phase of the breeding process starts with parent selection.The selection utilized in a GA is effective on the convergence speed of the algorithm.A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals.Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study.And the use of neural networks aims to produce better offspring during the GA search.The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring.In the breeding process, the first parent is selected based on the fitness value as usual.Then it is the neural network that decides the appropriate mate for the first parent chosen.Hence, the selection mechanism is not solely dependent on the fitness values in this study.The algorithm is tested with seven benchmark functions.It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is ef- fective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algo- rithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization.The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum.And the initial phase of the breeding process starts with parent selection.The selection utilized in a GA is effective on the convergence speed of the algorithm.A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals.Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study.And the use of neural networks aims to produce better offspring during the GA search.The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring.In the breeding process, the first parent is selected based on the fitness value as usual.Then it is the neural network that decides the appropriate mate for the first parent chosen.Hence, the selection mechanism is not solely dependent on the fitness values in this study.The algorithm is tested with seven benchmark functions.It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space (−1, 1) n , which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.

In this short note, we introduce a new architecture for spiking perceptron: The actual output is a linear combination of the firing time of the perceptron and the spiking intensity (the gradient of the state function) at the firing time. It is shown by numerical experiments that this novel spiking perceptron can solve the XOR problem, while a classical spiking neuron usually needs a hidden layer to solve the XOR problem.