Overlapping instruction subsets derived from human originated code have
previously been shown to dramatically shrink the inductive programming search
space, often by many orders of magnitude. Here we extend the instruction subset
approach to consider direct instruction-instruction applications (or
instruction digrams) as an additional search heuristic for inductive
programming. In this study we analyse the frequency distribution of instruction
digrams in a large sample of open source code. This indicates that the
instruction digram distribution is highly skewed with over 93% of possible
instruction digrams not represnted in the code sample. We demonstrate that
instruction digrams can be used to constrain instruction selection during
search, further reducing size of the the search space, in some cases by several
orders of magnitude. This significantly increases the size of programs that can
be generated using search based inductive programming techniques. We discuss
the results and provide some suggestions for further work.

通过考虑直接指令 - 指令应用作为搜索启发式的附加内容，本文将指令子集方法扩展到归纳编程中，分析了大量开源代码中指令二元组的频率分布，并证明指令二元组可以用于限制搜索期间的指令选择，从而显著减少搜索空间的大小，从而增加了使用基于搜索的归纳式编程技术生成程序的大小。

进一步利用指令二元组缩小归纳式编程的搜索空间

Further Decimating the Inductive Programming Search Space with  Instruction Digrams

With the development of deep learning (DL) techniques, rotating machinery
intelligent diagnosis has gone through tremendous progress with verified
success and the classification accuracies of many DL-based intelligent
diagnosis algorithms are tending to 100\%. However, different datasets,
configurations, and hyper-parameters are often recommended to be used in
performance verification for different types of models, and few open source
codes are made public for evaluation and comparisons. Therefore, unfair
comparisons and ineffective improvement may exist in rotating machinery
intelligent diagnosis, which limits the advancement of this field. To address
these issues, we perform an extensive evaluation of four kinds of models,
including multi-layer perception (MLP), auto-encoder (AE), convolutional neural
network (CNN), and recurrent neural network (RNN), with various datasets to
provide a benchmark study within the same framework. We first gather most of
the publicly available datasets and give the complete benchmark study of
DL-based intelligent algorithms under two data split strategies, five input
formats, three normalization methods, and four augmentation methods. Second, we
integrate the whole evaluation codes into a code library and release this code
library to the public for better development of this field. Third, we use
specific-designed cases to point out the existing issues, including class
imbalance, generalization ability, interpretability, few-shot learning, and
model selection. By these works, we release a unified code framework for
comparing and testing models fairly and quickly, emphasize the importance of
open source codes, provide the baseline accuracy (a lower bound) to avoid
useless improvement, and discuss potential future directions in this field. The
code library is available at
this https URL

本论文使用四种模型，多重感知器、编码器、卷积神经网络和循环神经网络，对各种数据拆分策略、输入格式、正则化和增强方式等进行了全面评估，并整合了代码库。通过这些工作，释放了一个公开的统一代码框架，以公平、快速地比较和测试模型，强调了开源代码的重要性，并讨论了这一领域的潜在未来方向。