集中式粗粒度分布并行模型和并行进化神经网络

提出了一种分布计算环境下并行进化神经网络的实现机制：集中式粗粒度模型。该模型基于并行遗传算法，可以同时对前馈神经网络的结构和权值进行优化。在分布计算环境中的实现为其在分布式网络中的应用开辟了广阔的前景。初步的实验结果表明该模型可以加快神经网络的进化速度，提高进化的效率

Concentrative Coarse-Grained Distributed Parallel Model and Parallel Evolutionary Neural Networks

Parallel Genetic Algorithms (PGAs) were used to simultaneously optimize the structure and weights for feed-forward neural networks. Aiming at its large-scale application in common distributed network system, a concentrative coarse-grained model for parallel evolutionary neural networks is designed and realized in a laboratorial distributed computation environment. The initial results of experiments indicate that the parallel model can quicken the searching process and improve the evolutionary efficiency.