Parameter tuning for configuring and analyzing evolutionary algorithms

By Agoston E. Eiben, Selmar K. Smit. Swarm and Evolutionary Computation, 2011

652 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2358139ba8bb50fe2e4f38c5d6200623d7de2843

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Citations in the corpus

Cuckoo search: recent advances and applications (2013, background): “In fact, parameter-tuning is an important area of research [12], which deserves more research attention.”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “In a different direction, Smith and Fogarty [198] applied GA self-adaptation to a steady state GA and investigated the influence of selection and crossover on the success of self-adaptation.”
Evolutionary Bilevel Optimization for Complex Control Tasks (2015, methodology): “A survey and systematic categorization of current existing techniques is provided by Eiben and Smit [18].”
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 .”
A bilevel optimization approach to automated parameter tuning (2014, methodology): “For a detailed overview, we refer the readers to a study by Eiben and Smit [14], which also provides a conceptual framework for parameter tuning along with categorizations for the existing approaches.”
Automatically improving the anytime behaviour of optimisation algorithms (2014, methodology): “…performance of optimisation algorithms, when designing and evaluating new algorithms and when tuning existing algorithms to specific problems (Birattari, 2009; Bartz-Beielstein, 2006; Hutter, Hoos, Leyton-Brown, & Stützle, 2009; Eiben & Smit, 2011; Hutter, Hoos, & Leyton-Brown, 2011; Hoos, 2012).”
Tunability: Importance of Hyperparameters of Machine Learning Algorithms (2018, background): “A related problem exists in many other algorithmic areas, e.g., control parameters in evolutionary algorithms (Eiben and Smit, 2011).”
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization (2013, methodology): “AccordingtoEibenandSmit[29],therearetwomethods forchoosingvaluesfortheparametersofevolutionaryalgorithms: parametertuningandparametercontrol.”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “Based on the parameter types classification in existing work [11], [38]–[40], parameters can be mainly classified into categorial and numerical parameters, according to their searchability [41], [42].”
Moth–flame optimization algorithm: variants and applications (2019, background): “For instance, parameter tuning one of the important components that used to enhance the performance of the algorithm [36].”