Population size reduction for the differential evolution algorithm
By Janez Brest, Mirjam Sepesy Maucec. Applied intelligence (Boston), 2008
337 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/3e3902a0e418075d548346ef0570d2ce0487916d
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
- Janez Brest
- Xin-She Yang
- Seyedali Mirjalili
- Pinar Çivicioglu
- Zhihua Cai
- Ryoji Tanabe
In Computational Theory and Mathematics
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Xin-She Yang
- Janez Brest
- Ryoji Tanabe
- Ferrante Neri
- Pinar Çivicioglu
- Zhihua Cai
Citations in the corpus
- Memetic algorithms and memetic computing optimization: A literature review (2012, background): “In [232], on the basis of the studies carried out in [15, 16, 18], a DE for LSPs has proposed.”
- Bio-inspired computation: Where we stand and what's next (2019, background): “This is a simple strategy that can render very good results [304, 305].”
- Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “In [11], the authors presented a method for gradually reducing population size of DE.”
- Single objective real-parameter optimization: Algorithm jSO (2017, background): “Recently, there have been several attempts to adjust this control parameter during the evolutionary process [6], [17].”
- DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, methodology): “The choice of the best population size of DE is always critical for different problems [19], [25], [26].”
- Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “The Differential Evolution with Population Size Reduction (DEPSR) employs, within a DE framework,avariablepopulationsizewhichisprogressivelyreducedduringtheoptimization process, see Brest and Mauycec (2008) .”
- Water wave optimization: A new nature-inspired metaheuristic (2015, background): “As most other EAs, WWO maintains a population of solutions, each of which is analogous to a “wave” having a height (or amplitude) hAZþ and a wavelength λARþ .”
- Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, background): “In [87], Brest and Maucec also stated the importance of reducing population size in order to improve the performance of DE.”
- Success-history based parameter adaptation for Differential Evolution (2013, methodology): “An extension of jDE which periodically reduces the population size by half in order to focus the search has been proposed [7].”
- Improving the search performance of SHADE using linear population size reduction (2014, methodology): “1 which uses DPSR instead of LPSR .”