DE/EDA: A new evolutionary algorithm for global optimization

By Jianyong Sun, Qingfu Zhang, Edward P. K. Tsang. Information Sciences, 2005

324 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/0078ed619fd450f5e92871f1c3892da2f3154e0d

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

1. Yaochu Jin
2. Ponnuthurai Nagaratnam Suganthan
3. Ran Cheng
4. Zhihua Cai
5. Shahryar Rahnamayan
6. Wenyin Gong
7. Swagatam Das
8. Xin Yao

In Biomedical Engineering

1. Zhihua Cai
2. Wenyin Gong
3. Xin Yao
4. Zhenyu Yang
5. Ke Tang
6. Charles X. F. Ling
7. D. Gong
8. Janez Brest

Citations in the corpus

• Opposition versus randomness in soft computing techniques (2008, background): “Value to reach (VTR) 10 8 [13,35] Mutation strategy DE/rand/1/bin [22,29,36,38,41] S.”
• Self-adaptive learning based particle swarm optimization (2011, methodology): “Besides, instead of using relatively fixed execution probabilities during the whole optimization procedure [54], SLPSO uses a gradual updating mechanism based on a given learning rate to adaptively update the execution probabilities based on the feedback of previous optimization procedure.”
• Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, methodology): “Sun et al. [21] combined DE with estimation of distribution algorithm (EDA), and proposed DE/EDA.”
• An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem (2014, background): “…applied to solve the permutation flow shop scheduling problem (Jarboui, Eddaly, and Siarry 2009; Zhang and Li 2011), the global continuous optimisation problem (Sun, Zhang, and Edward 2005), the nurse scheduling problem (Aickelin and Li 2007), LSFS problems (Pan and Ruiz 2012) and so on.”
• Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, methodology): “Sun et al. [20] proposed a combination of DE algorithms and the estimation of distribution algorithm (EDA), which tries to guide its search toward a promising area by sampling new solutions from a probability model.”
• DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, methodology): “[30] proposed a new hybrid algorithm based on a combination of DE with Estimation of Distribution Algorithm (EDA).”
• Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “Sun et al. [ 31 ] proposed DE/EDA, a hybrid of DE and estimation of distribution algorithm (EDA), in which new promising solutions are created by DE/EDA offspring generation scheme.”
• Large scale evolutionary optimization using cooperative coevolution (2008, methodology): “DE has been combined with other algorithms to produce various hybrid algorithms [20,29].”
• A Polynomial Chaos-Based Kalman Filter Approach for Parameter Estimation of Mechanical Systems (2010, unknown): “[27] proposed a DE/EDA hybrid approach.”
• Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion (2006, methodology): “EDAs have been successfully used in single-objective optimization problems [9], [10], [11].”
• Opposition-Based Differential Evolution (2008, background): “• Mutation strategy [2]: DE/rand/1/bin (classic version of DE) [2], [3], [15], [16], [26].”