Modular Grammatical Evolution for the Generation of Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Soltanian:2022:EC,

• author = "Khabat Soltanian and Ali Ebnenasir and Mohsen Afsharchi",
• title = "Modular Grammatical Evolution for the Generation of Artificial Neural Networks",
• journal = "Evolutionary Computation",
• year = "2022",
• volume = "30",
• number = "2",
• pages = "291--327",
• month = "Summer",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Modular Representation, ANN, NeuroEvolution",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/evco_a_00302",
• size = "37 pages",
• abstract = "This paper presents a novel method, called Modular Grammatical Evolution (MGE), towards validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, towards generating modular and multi-layer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class counts. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.",
• notes = "Department of Electrical and Computer Engineering, University of Zanjan, Zanjan 45371-38791, Iran",

}

Genetic Programming entries for Khabat Soltanian Ali Ebnenasir Mohsen Afsharchi

Citations