Root canal treatment in special needs patients was more likely to be carried out by specialist dental practitioners who were more likely to utilize a pharmacological approach for behaviour guidance and to perform single-visit root canal treatment compared with General Dentists. A multidisciplinary approach for special needs patients who require root canal treatment provides an opportunity for these patients to retain their dentition.
In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performance and understanding strengths and weaknesses is investigated. Furthermore, we explore the impact of various definitions of "good" performance. The visualization of the instance space provides an alternative method of algorithm discrimination by showing clusters of instances where algorithms perform well across the instance space. We validate the suitability of existing features and identify opportunities for future development.
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