Radial model of differential evolution dynamics

2020 ◽  
Author(s):  
Karol R. Opara
Keyword(s):  
Differential Evolution ◽  
Radial Model ◽  
Evolution Dynamics

Differential evolution dynamics analysis by complex networks

2015 ◽  
Vol 21 (7) ◽  
pp. 1817-1831 ◽  
Author(s):  
Lenka Skanderova ◽  
Tomas Fabian
Keyword(s):  
Differential Evolution ◽  
Complex Networks ◽  
Dynamics Analysis ◽  
Evolution Dynamics

scholarly journals Differential Evolution Dynamics Analysis Using Aggregated Networks

2017 ◽  
Vol 1 (1) ◽  
pp. 48
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Open Access ◽  
Differential Evolution ◽  
Original Work ◽  
Dynamics Analysis ◽  
Creative Commons ◽  
Population Evolution ◽  
Evolution Dynamics ◽  
Novel Algorithms ◽  
Open Access Article

In this paper, the dynamics of the selected variants of the differential evolution is modelled by aggregated network capturing the relationships between individuals established during the population evolution. The motivation of this research is to better understand the relationships between individuals of the selected variants of the differential evolution. Thanks to the analysis of the aggregated networks, the advantages as well as bottlenecks of the selected algorithms can be specified more precisely and the results of the analysis can be used to develop novel algorithms. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Differential Evolution Dynamics Modeled by Longitudinal Social Network

2017 ◽  
Vol 26 (3) ◽  
pp. 523-529 ◽  
Author(s):  
Lenka Skanderova ◽  
Tomas Fabian ◽  
Ivan Zelinka
Keyword(s):  
Social Network ◽  
Differential Evolution ◽  
Short Interval ◽  
Optimal Solution ◽  
Population Based ◽  
De Algorithm ◽  
Parent Selection ◽  
Evolution Dynamics ◽  
Population Improvement ◽  
Population Based Algorithm

AbstractDifferential evolution (DE) is a population-based algorithm using Darwinian and Mendel principles to find out an optimal solution to difficult problems. In this work, the dynamics of the DE algorithm are modeled by using a longitudinal social network. Because a population of the DE algorithm is improved in generations, each generation of DE algorithm is represented by one short-interval network. Each short-interval network is created by individuals contributing to population improvement. On the basis of this model, a new parent selection in the mutation operation is presented and a well-known benchmark set CEC 2013 Special Session on Real-Parameter Optimization (including 28 functions) is used to evaluate the performance of the proposed algorithm.

LZW differential evolution for binary encoding

2012 ◽  
Author(s):  
Orawan Watchanupaporn ◽  
Worasait Suwannik
Keyword(s):  
Differential Evolution ◽  
Binary Encoding
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