Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publisher : 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2015
Abstract : pDifferential Evolution (DE), an optimization algorithm under the roof of Evolutionary Algorithms (EAs), is well known for its efficiency in solving optimization problems which are non-linear and non-differentiable. DE has many good qualities such as algorithmic simplicity, robustness and reliability. DE also has the quality of solving the given problem with few control parameters (NP - population size, F - mutation rate and Cr - crossover rate). However, suitable setting of values to these parameters is a complicated task. Hence, various adaptation strategies to tune these parameters during the run of DE algorithm are proposed in the literature. Choosing the right adaptation strategy itself is another difficult task, which need in-depth understanding of existing adaptation strategies. The aim of this paper is to summarize various adaptation strategies proposed in DE literature for adapting F and Cr. The adaptation strategies are categorized based on the logic used by the authors for adaptation, and brief insights about each of the categories along with the corresponding adaptation strategies are presented. © 2015 IEEE./p