A Survey of Parallel Genetic Algorithms
By Erick Cantú-Paz. 2000
1,161 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/a13fcba4fab4713d5acb2d970eddc6b148d2596d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Ran Cheng
- Yaochu Jin
- Zhi-hui Zhan
- Ponnuthurai Nagaratnam Suganthan
- Carlos Artemio Coello Coello
- Swagatam Das
- Jun Zhang
- Ke Tang
Citations in the corpus
- Hybrid Genetic Algorithms: A Review (2006, methodology): “Hybridization in addition to parallelization [29], time utilization [30], and evaluation relaxation (function approximation) can be used to speed up a genetic search [31].”
- 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.”
- Mathematical discoveries from program search with large language models (2023, methodology): “For this, we use the islands model, a parallel genetic algo-rithm 27,28 .”
- A Hybrid Neurogenetic Approach for Stock Forecasting (2007, background): “Parallel genetic algorithms can be categorized into three classes [6]: (i) global singlepopulation master-slave GAs, (ii) single-population fine-grained, and (iii) multiple-population coarse-grained GAs.”
- Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art (2000, methodology): “Deb and Goldberg (1989) then implemented phenotypic-based restricted mating in their GA niching and sharing investigation.”
- A survey on new generation metaheuristic algorithms (2019, background): “Cantú-Paz (1998) provides detailed information about parallel GA.”
- Parallelism and evolutionary algorithms (2002, background): “( See details on this and the rest of models also in [21].)”
- Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009, background): “Introducing imbalance between the contribution of various subcomponents, subcomponents with nonuniform sizes, and conforming and conflicting overlapping functions are among the major new features proposed in this report.”
- Evolutionary Algorithms for Solving Multi-Objective Problems (2002, background): “Island model GAs also implement restricted mating but in a geographic sense where solutions mate only with neighbors residing within some restricted topology (Cantu-Paz, 1997).”