Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms
This work addresses automatic programming for researchers in machine learning, but it is incremental as it applies known swarm intelligence algorithms to a specific domain.
The paper tackled automatic program generation by proposing two swarm programming methods, GMFO and GWO, based on moth-flame and whale optimization algorithms, and tested them on benchmark problems like Santa Fe Ant Trail, showing they can generate programs effectively compared to existing methods like GBC and GFWA.
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.