Semantic variation operators for multidimensional genetic programming.
Semantic variation operators for multidimensional genetic programming.
复制标题
多维遗传规划的语义变异算子。
DOI：
10.1145/3321707.3321776
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Moore,JasonH
中科院分区：
文献类型：
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作者：
LaCava,William;Moore,JasonH
多维遗传规划代表候选解决方案的程序集，从而提供了一个有趣的框架，利用积木识别。为了实现这一目标，我们研究了使用机器学习作为一种方式，以偏见的程序组件被提升，并提出了两个语义操作符，以选择有用的构建块放置在交叉。我们提出的一个前向阶段交叉算子导致了一组回归问题的显着改善，并在一个大型基准研究中产生了最先进的结果。我们讨论这个架构和其他人在他们的倾向，允许启发式搜索，利用信息在进化过程中。最后，我们看看共线性和复杂性的数据表示，从这些架构的结果，以期解开应用程序中的变化因素。
Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.