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Table 1:
Inclusion and exclusion criteria.
No.Criterion
IC1 The study describes an extension of NEAT, (x-NEAT) 
IC2 The study describes a hybrid model, for example, based on NEAT and another evolutionary or machine learning algorithm 
EC1 The study is only an application of an x-NEAT method to a dataset or a new domain 
EC2 The study describes an EA that is not a successor of NEAT but inspired by the NEAT principles (Section 3.1) 
EC3 The study compares a not-NEAT-based NE method with NEAT, or existing x-NEAT methods among each other 
EC4 The study is an older/conference version of a relevant journal paper 
No.Criterion
IC1 The study describes an extension of NEAT, (x-NEAT) 
IC2 The study describes a hybrid model, for example, based on NEAT and another evolutionary or machine learning algorithm 
EC1 The study is only an application of an x-NEAT method to a dataset or a new domain 
EC2 The study describes an EA that is not a successor of NEAT but inspired by the NEAT principles (Section 3.1) 
EC3 The study compares a not-NEAT-based NE method with NEAT, or existing x-NEAT methods among each other 
EC4 The study is an older/conference version of a relevant journal paper 

IC: Inclusion Criterion, EC: Exclusion Criterion.

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