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A Systematic Literature Review of the Successors of ‘NeuroEvolution of Augmenting Topologies’

Tijdschriftbijdrage - Tijdschriftartikel

NeuroEvolution (NE) refers to a family of methods for optimizingArtificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper’s relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT’s successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

Tijdschrift: Evolutionary Computation
ISSN: 1063-6560
Issue: 1
Volume: 29
Pagina's: 1-73
Jaar van publicatie:2021
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:2
Auteurs:Regional
Authors from:Higher Education
Toegankelijkheid:Open