Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis

By Yee Leung, Yong Gao, Zongben Xu. 1997

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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “CONVOLUTIONAL neural networks (CNNs) [1], [2], as the most dominant deep learning approaches [3], have been demonstrating their promising performance in addressing the various real-world applications, such as image classification [4], speech understanding [5], and natural language processing [6], to”
Parameter extraction of solar photovoltaic modules using penalty-based differential evolution (2012, background): “One issue that is of major concern in all the above-mentioned methods is premature convergence [37,38] which relates to the loss of diversity in the population.”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, background): “On the other hand, the metaheuristics are good in global search, but they suffer premature convergence [124, 125].”
Completely Automated CNN Architecture Design Based on Blocks (2020, background): “Theoretically, a good population has the characteristics of both convergence and diversity [30] to prevent from trapping into local minima [48], [49] and premature convergence [44], [45].”
A review of population-based meta-heuristic algorithm (2013, background): “Although GA has been extensively used in various problems, it suffers from some disadvantages [49, 50, 51] such as: 1.”