Introduction to Evolutionary Algorithms

By Keith L. Downing. 2006

465 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a1a47e804277d6e7762bf85ddea8e6c54f33427e

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Citations in the corpus

Optimal power flow: a bibliographic survey II (2012, background): “Genetic Algorithms (GAs) are evolutionary search algorithms based on the mechanics of natural genetics [69, 154].”
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 (2021, methodology): “1 Evolutionary algorithms (EAs) (Yu and Gen, 2010) are another popular approach to black-box is optimization; they include methods such as differential evolution (DE), genetic algorithms (GAs), and the covariance matrix adaptation evolution strategy (CMAES) (Hansen et al.”
Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning (2013, methodology): “The fitness of each solution is then evaluated and the mating pool selected favoring fit parents using the stochastic universal sampling method presented in [18].”
Comparison of Three Evolutionary Algorithms: GA, PSO, and DE (2012, background): “For a more recent in depth discussion of evolutionary algorithm, see Yu and Gen (2010).”
Optimization of sensor placement for structural health monitoring: a review (2019, methodology): “The results indicate that the BSSP and FSSP techniques are sensitive to the initial candidate locations and also the number of sensors, but given the right starting conditions, they have the ability to outperform the GA.”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “Good resources for further details are: [13], which is also available in German [29], and [30], [31], [32], [33].”