Parallelism and evolutionary algorithms

By Enrique Alba, Marco Tomassini. 2002

866 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, background): “In addition to (1) and (2), to attain higher speeds through parallelism, we distribute experiments across worker processes that exchange models through migration [1].”
Parallel metaheuristics: recent advances and new trends (2012, background): “Among the most widely known types of structured metaheuristics, the " distributed " (or coarse-grain) and " cellular " (or fine-grain) algorithms are very popular optimization procedures (Alba and Tomassini, 2002).”
Practical Genetic Algorithms (2005, methodology): “For more information on these selection methods, see Bäck (1994) and Goldberg and Deb (1991).”
Dynamic Security Risk Management Using Bayesian Attack Graphs (2012, background): “Further, these algorithms are inherently parallel and can easily be adapted to utilize the processing power of most massively parallel systems [37].”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “◦ more sophisticated components (genetic operators, adaptation, : : :), and ◦ new and advanced evolutionary models, multiobjetive genetic algorithms [28,40], coevolutionary algorithms [109], parallel genetic algorithms [3,19], estimation distribution algorithms [89], etc.”
Bio-inspired computation: Where we stand and what's next (2019, background): “systems in order to improve the performance of sequential (traditional) evolutionary solvers [207,208].”
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005, neutral): “Among the many types of structured EAs, distributed and cellular algorithms are two popular optimization tools [2], [3] Manuscript received June 16, 2003; revised October 14, 2004.”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “A comprehensive classification and presentation can be found in [50], [51].”
MOCell: A cellular genetic algorithm for multiobjective optimization (2009, background): “Among the many types of structured EAs, distributed and cellular models are two popular optimization variants [4, 5] (see Fig.”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “At this point, we must remark the existence of efficient parallel GAs [1] as a kind of GAs that would be evaluated for designing GFSs for large databases putting special emphasis on aspects of scalability and efficiency.”
Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters (2019, background): “to provide significant performane benefits for parallel evolutionary algorithms [27].”
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, background): “(1) Compared with [3,16,4,131] published ten years ago, this survey introduces and describes more recent works in this area.”