# Global Optimization for Neural Network Training

@article{Shang1996GlobalOF, title={Global Optimization for Neural Network Training}, author={Yi Shang and Benjamin W. Wah}, journal={Computer}, year={1996}, volume={29}, pages={45-54} }

We propose a novel global minimization method, called NOVEL (Nonlinear Optimization via External Lead), and demonstrate its superior performance on neural network learning problems. The goal is improved learning of application problems that achieves either smaller networks or less error prone networks of the same size. This training method combines global and local searches to find a good local minimum. In benchmark comparisons against the best global optimization algorithms, it demonstrates…

## 198 Citations

### Deterministic global optimization for FNN training

- Computer ScienceIEEE Trans. Syst. Man Cybern. Part B
- 2003

Numerical comparison with benchmark problems from the neural network literature shows superiority of the proposed algorithm over some local methods, in terms of the percentage of trials attaining the desired solutions.

### ALTERNATIVES TO GRADIENT-BASED NEURAL TRAINING

- Computer Science
- 1999

Several new global optimization methods suitable for architecture optimization and neural training are described here, including multistart initialization methods offered as an alternative to global minimization.

### Global Optimisation of Neural Networks Using a Deterministic Hybrid Approach

- Computer ScienceHIS
- 2001

Preliminary experimentation results show that the proposed deterministic approach could provide near optimal results much faster than the evolutionary approach.

### Optimization and global minimization methods suitable for neural networks

- Computer Science
- 1999

A survey of global minimization methods used for optimization of neural structures and network cost functions, including some aspects of genetic algorithms, are provided.

### Constrained Formulations for Neural Network Training and Their Applications to Solve the Two-Spiral

- Computer Science
- 2000

It is shown that constraints violated during a search provide additional force to help escape from local minima using the newly developed constrained simulated annealing (CSA) algorithm.

### Global optimization issues in deep network regression: an overview

- Computer ScienceJ. Glob. Optim.
- 2019

An overview of global issues in optimization methods for training feedforward neural networks (FNN) in a regression setting is presented and some recent results on the existence of non-global stationary points of the unconstrained nonlinear problem are reviewed.

### Training multilayer neural networks using fast global learning algorithm - least-squares and penalized optimization methods

- Computer ScienceNeurocomputing
- 1999

### First-Order Optimization Method for Single and Multiple-Layer Feedforward Artificial Neural Networks

- Computer Science
- 2010

The first-order optimization method is applied in single and multiple-layer feedforward artificial neural networking problem and some useful results are obtained.

### Training Recurrent Neural Networks as a Constraint Satisfaction Problem

- Computer Science, MathematicsArXiv
- 2018

This study converts the training set of a neural network into a CSP and uses the quotient gradient system to find its solutions and compares it to a genetic algorithm and error backpropagation.

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