Reference line-based Estimation of Distribution Algorithm for many-objective optimization

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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “lems [19]–[21], mainly because of its gradient free and insensitiveness to local minimum [22]–[24].”
Completely Automated CNN Architecture Design Based on Blocks (2020, background): “…adopting the best ones as the parents could easily cause the loss of diversity in the population, which in turn leads to the premature convergence [44], [45], and as a result, the best performance of the population cannot be achieved [46], [47] due to trapping into the local minima [48], [49].”
IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, methodology): “In addition, a reference line-based estimation of distribution algorithm was introduced in [12] for explicitly promoting the diversity of an MaOEA.”
A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, methodology): “, MaOEDA/RL) [10], the improved reg-”
Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018, background): “…include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27], [28], [29], [30], [31].”