Optimizing multi-instance neural networks based on an improved genetic algorithm
基于改进遗传算法的多示例神经网络优化

In order to achieve higher predictive accuracy, an improved genetic algorithm for optimizing multi-instance neural networks was presented. Convergence rate was increased and premature convergence was overcome by means of local search operator, suppress operator and adaptive calculations of probabilities for operators. Some experiments on well-known test data show that multi-instance neural networks that are optimized by the improved genetic algorithm heighten significantly predictive accuracy and computational expensiveness of the algorithm is less than other algorithms.