Recent advances in differential evolution - An updated survey

By Swagatam Das, Sankha Subhra Mullick, Ponnuthurai Nagaratnam Suganthan. Swarm and Evolutionary Computation, 2016

1,504 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/17051ac305a6aaefb1cf7808e8e560f751f82b18

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. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Janez Brest
4. Xin-She Yang
5. Seyedali Mirjalili
6. Pinar Çivicioglu
7. Seyed Mohammad Mirjalili
8. Francisco Herrera

In Computational Theory and Mathematics

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Janez Brest
4. Xin-She Yang
5. Seyedali Mirjalili
6. Seyed Mohammad Mirjalili
7. Pinar Çivicioglu
8. Mirjam Sepesy Maucec

Citations in the corpus

• Bio-inspired computation: Where we stand and what's next (2019, background): “For instance, the most successful proposals in the family of DE-based heuristics during the last few years hinge on the adaptation of the main parameters of this bio-inspired solver, namely, the F (differential weight) and CR (crossover probability) parameters [303].”
• Evolutionary algorithms and their applications to engineering problems (2020, background): “In EAs, there are many open research problems, which are discussed in more detail in [53, 78].”
• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers (2024, neutral): “For example, some recent DE-variants have been investigating adaptive control parameters.”
• Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, background): “Differential Evolution is known to be highly dependent on the choice of parameter settings for its control parameters [7].”
• Red fox optimization algorithm (2021, background): “An extensive study of these methods concerning latest achievements and a variety of possible applications and theoretical backgrounds was presented in Das et al. (2016). The number of possibilities for potential applications is huge since heuristic methods give good accuracy of results with ease of ”
• LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “The practices in the fields of parameter adaptation demonstrate the relationship between the problem itself and the parameter values [5].”
• Single objective real-parameter optimization: Algorithm jSO (2017, methodology): “A huge number of improvements were proposed recently [5].”