What are the limitations of using APFD as a metric for fitness function in various applications?
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Using APFD as a metric for fitness functions in various applications presents limitations. While Search-based Automated Program Repair (APR) has shown success in bug fixing, the study by Bian et al. suggests that more fine-grained fitness functions, like 2Phase, incorporating test case failures alongside Boolean results, may not significantly outperform standard fitness functions. Additionally, Sakti et al. highlight the challenge of insufficiently informed fitness functions in search-based software testing, particularly in guiding searches within nested predicates, proposing new fitness functions based on constraint programming concepts. Furthermore, Vanneschi et al. caution that the Fitness-Proportional Negative Slope Coefficient, a measure of fitness landscapes, may not always accurately predict problem difficulty, as some landscapes may be easy to solve despite limited evolvability. These findings collectively underscore the need for more nuanced and informed fitness functions in diverse applications.
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| Papers (5) | Insight |
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| The limitations of using APD as a metric include reduced accuracy due to leaks, minimal impact from oronasal masks, and increased resistance affecting measurements, especially in ventilated patients. | |
08 Jul 2009 9 Citations | Not addressed in the paper. |
22 Apr 2019 6 Citations | Not addressed in the paper. |
01 Jun 2021 8 Citations | Not addressed in the paper. |
18 May 2013 9 Citations | Not addressed in the paper. |
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