This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network...

The paper deals with continuously operating optimization neural netw orks with lossy dynamics.As the main feature of the neural model time-varying nature of neuron activation functions is introduced. The model presented is general in the sense that it covers the cases of neural net w orks for combinatorial optimization (Hop eld-like netw orks) and neural models for optimization problems with co...

In this paper, a Hopfiled neural network for nonlinear constrained optimization problem is discussed. The energy function for the nonlinear neural network with its neural dynamics is defined based on penalty function with two-order continuous differential. The system of the neural network is stable, and its equilibrium point of the neural dynamics is also an approximately solution for nonlinear...

A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is app...

|This paper proposes a new neural architecture (Nessy) which uses evolutionary optimization for learning. The architecture, the outline of its evolutionary algorithm and the learning laws are given. Nessy is based on several modi cations of the multilayer backpropagation neural network. The neurons represent genes of evolutionary optimization, refered to as solutions. Weights represent probabil...

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. M...

Problemspecific neural networks, multimembered evolutionary strategy, multicriteria optimization, pruning of neural networks, damage analysis, solution of inhomogeneous linear equation systems using problemspecific neural networks Abstract The integration of neural networks and a multimembered evolutionary strategy leads to a new multicriteria optimization approach for plane truss structures. C...