In this research, we compare four different evaluation methods in coevolution on the Majority Function problem. The size of the problem is selected such that evaluation against all possible test cases is feasible. Two measures are used for the comparisons, i.e., the objective fitness derived from evaluating solutions against all test cases, and the objective fitness correlation (OFC), which is defined as the correlation coefficient between subjective and objective fitness. The results of our experiments suggest that a combination of average score and weighted informativeness may provide a more accurate evaluation in coevolution. In order to confirm this difference, a series of t-tests on the preference between each pair of the evaluation methods is performed. The resulting significance is affirmative, and the tests for two quality measures show similar preference on four evaluation methods. %This study is the first time OFC is actually computed on a real problem. Experiments on Majority Function problems with larger sizes and Parity problems are in progress, and their results will be added in the final version.
翻译:在此研究中,我们比较了在多数功能问题上演进的四种不同的评价方法。 问题的规模是选择了对所有可能的试验案例的评价是可行的。 在比较时使用了两种措施, 即对所有试验案例的评价解决办法所得出的客观优缺点, 以及客观的健身关系(OFC), 被定义为主观和客观健康之间的相关系数。 我们的实验结果表明, 平均分数和加权信息化相结合, 可能会在演进中提供更准确的评价。 为了证实这一差异, 进行了一系列关于每一对评价方法之间偏好的测试。 其结果是肯定的, 对两种质量措施的测试显示对四种评价方法的相似偏好。% 本研究是首次在实际问题上进行计算。 对较大和对等性问题的多数功能问题的实验正在进行中, 其结果将在最后版本中添加 。