Evolution algorithms in combinatorial optimization

By Heinz Mühlenbein, M. Gorges-Schleuter, Ottmar Krämer. 1988

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Technical report on Conversational Question Answering (2019, methodology): “Therefore they are widely used in combinatorial optimization and operation research problems(Deb, 1998; Mühlenbein et al., 1988).”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, methodology): “The algorithms thus obtained are sometimes called " genetic local searches " (Mühlenbein et al. 1988) or " memetic algorithms " (Moscato 1989, Moscato and Cotta 2010).”
The evolvability of artificial neural net-works for robot control (2002, methodology): “In population based evolutionary methods, there is considerable interest in the use of hybrid algorithms, typically incorporating some form of greedy hill-climbing into the evolutionary process (see for example Brady, 1985; Mühlenbein et al., 1988).”
Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, background): “EAs offer immense flexibility in accommodating diverse data representations (be it continuous or combinatorial [32]), and scale fairly well to higher dimensions [33], [34].”
USPEX - Evolutionary crystal structure prediction (2006, background): “, for the traveling salesman problem [14] and the problem of cluster optimization [15].”
Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, unknown): “searches [45], and combinatorial representations [46].”