The quality of each relevant paper was evaluated according to the Quality Assessment Criteria (QAC) defined in the format of questions and presented in Table 2. If a question was addressed by the paper, it could take one of the answers: “yes,” “partly,” and “no,” which received one, one-half,^{} and zero points, respectively. If the question was not addressed at all by the paper, then it received the answer “non-applicable” excluding it from the calculation of the final result. In this way, a paper's final score was calculated by averaging the score of each question. Nine papers had a score $\u2208[0,0.5]$ and they were excluded from the study.

Table 2:

QA questions concerning the x-NEAT's principles | |

QA1 | Are the aims of the research clearly defined? |

QA2 | Are the main aspects of the proposed method explained in detail? |

QA3 | If the method introduces a new encoding scheme, is it described clearly? If the same encoding as in previous methods is used, is it understandable from the paper? |

QA questions regarding the experimental procedure | |

QA4 | Is the experimental procedure clearly described? |

QA5 | Is the method evaluated on sufficient number of datasets? (number of datasets $\u22653$: yes, 2: partly, 1: no) |

QA6 | If the study involves a custom artificial dataset, is its construction method adequately described? If it cannot be described for example, in case of a video game, is the task clearly explained? |

QA7 | Are the parameters of the NE algorithm clearly described? |

QA8 | Are the metrics used for measuring the algorithm's performance clearly defined? |

QA9 | Is each experiment run for an adequate number of repetitions? (Yes: $\u226520$, Partly: [10,20), No: [0,10)) |

QA10 | Is there a statistical test to test if a statistical difference in the compared performances exists? |

QA11 | Is the proposed method compared to the state of the art of NE methods? |

QA12 | Is the proposed method compared to other machine learning/EC algorithms? |

QA questions regarding the reception of the paper from the community | |

QA13 | Does the study have an adequate number of citations per year?^{2} (Yes: $\u22651$, Partly: [0.5,1), No: [0,0.5)) |

QA questions concerning the x-NEAT's principles | |

QA1 | Are the aims of the research clearly defined? |

QA2 | Are the main aspects of the proposed method explained in detail? |

QA3 | If the method introduces a new encoding scheme, is it described clearly? If the same encoding as in previous methods is used, is it understandable from the paper? |

QA questions regarding the experimental procedure | |

QA4 | Is the experimental procedure clearly described? |

QA5 | Is the method evaluated on sufficient number of datasets? (number of datasets $\u22653$: yes, 2: partly, 1: no) |

QA6 | If the study involves a custom artificial dataset, is its construction method adequately described? If it cannot be described for example, in case of a video game, is the task clearly explained? |

QA7 | Are the parameters of the NE algorithm clearly described? |

QA8 | Are the metrics used for measuring the algorithm's performance clearly defined? |

QA9 | Is each experiment run for an adequate number of repetitions? (Yes: $\u226520$, Partly: [10,20), No: [0,10)) |

QA10 | Is there a statistical test to test if a statistical difference in the compared performances exists? |

QA11 | Is the proposed method compared to the state of the art of NE methods? |

QA12 | Is the proposed method compared to other machine learning/EC algorithms? |

QA questions regarding the reception of the paper from the community | |

QA13 | Does the study have an adequate number of citations per year?^{2} (Yes: $\u22651$, Partly: [0.5,1), No: [0,0.5)) |

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