论文标题
使用飞蛾优化和鲸鱼优化算法的群编程
Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms
论文作者
论文摘要
自动编程（AP）是机器学习（ML）的重要领域，该领域会自动生成计算机程序。 AP中新兴的研究领域的Swarm编程（SP）将使用Swarm Intelligence（SI）算法自动生成计算机程序。本文提出了两种基于语法的SP方法，称为语法飞蛾优化器（GMFO）和语法鲸鱼优化器（GWO）。蛾 - 挡板优化器和鲸鱼优化算法分别用作搜索引擎或GMFO和GWO中的学习算法。提出的方法在圣达菲蚂蚁步道，四分之一的符号回归和三输入多路复用器问题上进行了测试。将结果与语法蜜蜂菌落（GBC）和语法烟花算法（GFWA）进行了比较。实验结果表明，所提出的SP方法可用于自动计算机程序生成。
Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research area in AP, automatically generates the computer programs using Swarm Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Optimizer (GMFO) and Grammatical Whale Optimizer (GWO). The Moth-Flame Optimizer and Whale Optimization algorithm are used as search engines or learning algorithms in GMFO and GWO respectively. The proposed methods are tested on Santa Fe Ant Trail, quartic symbolic regression, and 3-input multiplexer problems. The results are compared with Grammatical Bee Colony (GBC) and Grammatical Fireworks algorithm (GFWA). The experimental results demonstrate that the proposed SP methods can be used in automatic computer program generation.
