Theory of Genetic Algorithms

By Thomas Bäck, J. Graaf, Joost N. Kok, W. Kosters. 2001

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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “This will become even more severe in the case of a large population and with a large number of generations, which is a more widely used configuration of evolutionary algorithms targeting at finding the better solutions [16], [18], [27].”
Completely Automated CNN Architecture Design Based on Blocks (2020, background): “GAs address optimization problems by imitating the biological evolution through a series of bio-inspired operators, such as crossover, mutation, and selection [35], [36].”
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018, background): “…evolutionary algorithms 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]–[31].”
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].”