论文标题
深人造神经网络的增量进化和发展
Incremental Evolution and Development of Deep Artificial Neural Networks
论文作者
论文摘要
神经进化（NE）方法以将进化计算应用于人工神经网络（ANN）的优化而闻名。尽管有帮助非专家用户设计和训练ANN，但绝大多数NE方法忽略了在解决其他任务时收集的知识，即，进化从头开始，每个问题都从头开始，最终延迟了进化过程。为了克服这一缺点，我们将快速深度进化网络结构化表示（快速数据）扩展到增量开发。我们假设，通过转移从先前的任务中获得的知识，我们可以获得卓越的结果和加速发展。结果表明，通过增量开发产生的模型的平均性能在统计学上优于非额外的平均表现。如果通过增量开发进行的评估数量小于非额外开发所执行的评估数量，则获得的结果在性能上相似，这表明增量开发加快了进化的速度。最后，使用增量开发产生的模型可以更好地推广，因此，在没有进一步发展的情况下，就看不见的问题报告了卓越的性能。
NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks(ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.
