Parameter Control in Evolutionary Algorithms: Trends and Challenges

By Giorgos Karafotias, Mark Hoogendoorn, Agoston E. Eiben. IEEE Transactions on Evolutionary Computation, 2015

468 citations recorded by the source corpus.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Bio-inspired computation: Where we stand and what's next (2019, background): “The excellent survey in [316] describes in depth different parameter control strategies reported in recent times.”
The irace package: Iterated racing for automatic algorithm configuration (2016, background): “Due to the separation between a tuning and a production phase, automatic algorithm conﬁguration is also known as oﬄine parameter tuning to differentiate it from online approaches that adapt or control parameter settings while solving an instance [13,50] .”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “8) Other interesting issues include analysis of fitness function [54], parameter control [55], locating multiple optimal solutions of nonlinear equation systems [56], using visualization of Pareto-front approximations [57], decomposition [58], two-archive algorithm [59], and reduced objective comput”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, background): “These control strategies could be deterministic, adaptive, or self-adaptive [21].”
From evolutionary computation to the evolution of things (2015, methodology): “Techniques such as automated parameter tuning 64,65,66,67 and adaptive parameter control continue to make advances in this area 68,69,70,71 .”