Publication Type:

Conference Paper

Source:

2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015, Institute of Electrical and Electronics Engineers Inc. (2015)

ISBN:

9781479978489

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965078699&partnerID=40&md5=7c3a4e8cc3385062c5835e5417e24b96

Keywords:

Algorithms, Artificial intelligence, Computation theory, Crossover rates, Differential Evolution, Differential evolution algorithms, Evolutionary algorithms, Evolutionary algorithms (EAs), In-depth understanding, Mutation rates, Optimization, Optimization algorithms, Parameter adaptation, Parameter estimation, Population statistics, Quality control

Abstract:

<p>Differential 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>

Notes:

cited By 0; Conference of 6th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015 ; Conference Date: 10 December 2015 Through 12 December 2015; Conference Code:120030

Cite this Research Publication

P. Pranav and Jeyakumar, G., “Control parameter adaptation strategies for mutation and crossover rates of differential evolution algorithm - An insight”, in 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015, 2015.

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