Differential Evolution: A Survey of the State-of-the-Art

By Swagatam Das, Ponnuthurai Nagaratnam Suganthan. IEEE Transactions on Evolutionary Computation, 2011

5,100 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b630e7344ebd9d7e1386cb08c9e9fc03c910380c

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Citations in the corpus

Multiple documents summarization based on evolutionary optimization algorithm (2013, methodology): “Then, a mutant vector Vp(t) = [vp,1(t), . . . , vp,n(t)] is generated by adding the weighted difference of two vectors to the third: VpðtÞ ¼ Up1ðtÞ þ F ðUp2ðtÞ Up3ðtÞÞ; ð13Þ where F is called mutation factor which controls the amplification of the differential variation (Up2(t) Up3(t)).”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “Moreover, as can be observed in literature, several claims and counter-claims were reported concerning the setting of the 14 control parameters [14].”
Recent advances in differential evolution - An updated survey (2016, methodology): “In this respect, the first comprehensive survey on almost all aspects of the DE family of algorithms was published in 2011 by [40].”
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018, methodology): “Su et al. computed the adversarial examples by using the concept of Differential Evolution [148].”
Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model (2012, background): “…are considered under soft computing techniques where as the various evolutionary learning algorithms include Genetic algorithm(GA) [9], Ant Colony Optimization(ACO), Particle Swarm Optimization (PSO) [11][12][13], Differential Evolution (DE)[14][15], Bacterial Foraging Optimization (BFO)[16] etc.”
UAV-Assisted Data Collection for Internet of Things: A Survey (2022, methodology): “Differential Evolution Algorithm: Similar to GA, DE is a kind of intelligent optimization search through the cooperation and competition among individuals in the population [130].”
One Pixel Attack for Fooling Deep Neural Networks (2017, neutral): “DE is a population-based optimization algorithm for solving complex multi-modal optimization problems [6], [23].”
Single objective real-parameter optimization: Algorithm jSO (2017, methodology): “In recent decades, it has shown robustness, efficiency, and it was very competitive when it was applied to solve real-world optimization problems [3], [4].”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, methodology): “They used Differential Evolution (DE) [58] to estimate the location and RGB value of the pixel to be modified in the image to create an adversarial image, where the fitness criterion of the evolution is defined by accounting for the model prediction.”
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers (2024, neutral): “…regarded evolutionary algorithms (Holland, 1975; 1992; Mitchell, 1998; Mirjalili et al., 2020) and DE has emerged as one of the most widely utilized algorithms for complex optimization challenges since its inception (Storn & Price, 1997; Price, 2013; Das & Suganthan, 2010; Pant et al., 2020).”
Stable Matching-Based Selection in Evolutionary Multiobjective Optimization (2014, methodology): “In this paper, we use the differential evolution (DE) operator [27] and polynomial mutation [28] as in [13].”
A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads (2014, methodology): “In addition, some other commonly used algorithms which include weighted PSO [25], PSO-CF [26] and classical DE/ rand/1/bin [27] are selected for the comparative study.”