Influence of crossover on the behavior of Differential Evolution Algorithms

By Daniela Zaharie. Applied Soft Computing, 2009

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics

View Paper: https://www.semanticscholar.org/paper/cc5c24681ca0149bf027e4bee1f29f0f02bf5151

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. Rammohan Mallipeddi
4. Pinar Çivicioglu
5. Michael G. Epitropakis
6. Guohua Wu
7. Huangke Chen
8. Michael N. Vrahatis

In Genetics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das

Citations in the corpus

• Differential Evolution: A Survey of the State-of-the-Art (2011, background): “Very recently in [115], the influence of the crossover rate on the distribution of the number of mutated components and on the probability for a component to be taken from the mutant vector (mutation probability) is theoretically analyzed for several variants of crossover, including classical binomi”
• Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “The mutation factor controls the amplification of the difference between two individuals and is used to prevent the stagnation of the search process.”
• Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “Crossover rate CR (0 ≤ CR ≤ 1) controls the number of components in each individual to be mutated in the current population [66].”