Self-adaptive differential evolution with neighborhood search
By Zhenyu Yang, Ke Tang, Xin Yao. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
380 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/4bc5f4d739b46126c0cf5c3a6300315edfcf51e2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Pinar Çivicioglu
- Ferrante Neri
- Sankha Subhra Mullick
- Ville Tirronen
- Yaochu Jin
- Xin Yao
In Computational Theory and Mathematics
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Pinar Çivicioglu
- Ferrante Neri
- Ville Tirronen
- Sankha Subhra Mullick
- Xin Yao
- Shahryar Rahnamayan
Citations in the corpus
- Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, background): “Somewhat similar in spirit to the present paper is the study by [34] on self-adaptive differential evolution with neighborhood search (SaNSDE).”
- DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, methodology): “The component optimizer of both frameworks is SaNSDE [67], and the population size of all algorithms is set to 50 as suggested by Yang et al. [67].”
- JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “Motivated by this consideration, different adaptive or self-adaptive mechanisms [9]–[18] have been recently introduced to dynamically update the control parameters without a user’s prior knowledge of the relationship between the parameter setting and the characteristics of optimization problems.”
- Large scale evolutionary optimization using cooperative coevolution (2008, methodology): “A new algorithm, the self-adaptive NSDE (SaNSDE), combines the advantages of NSDE and SaDE together in a single algorithm [26].”
- Recent advances in differential evolution - An updated survey (2016, methodology): “[195] extended the DE Enhanced by Neighborhood Search (DENS) [214] for large scale global optimization in the following way.”
- Metaheuristics in large-scale global continues optimization: A survey (2015, background): “An n-dimensional objective vector is partitioned randomly into multiple low dimensional subcomponents with respect to predetermined sizes; each subcomponent is evolved by a self-adaptive DE with the neighborhood search algorithm (namely SaNSDE) [202].”
- Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, methodology): “The population size is 50 as suggested in [48].”
- Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “Clearly, the risk of the DE stagnation is higher for larger decision spaces and worsens as the number of dimensions of the problem increases (see Zamuda et al. 2008, Olorunda and Engelbrecht 2007, Yang et al. 2007, 2008a, Noman and Iba 2005, Gao and Wang 2007, and Brest et al. 2008).”
- Differential Evolution With Ranking-Based Mutation Operators (2013, neutral): “[12] proposed a neighborhood search based DE.”