Towards Better Population Sizing for Differential Evolution Through Active Population Analysis with Complex Network

2017 ◽  
pp. 225-235 ◽  
Author(s):  
Adam Viktorin ◽  
Roman Senkerik ◽  
Michal Pluhacek ◽  
Tomas Kadavy
Keyword(s):  
Differential Evolution ◽  
Complex Network ◽  
Population Analysis ◽  
Active Population

Differential Evolution Dynamic Analysis in the Form of Complex Networks

2016 ◽  
pp. 285-318 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Complex Networks ◽  
Dynamic Analysis ◽  
Evolutionary Algorithm ◽  
Complex Network ◽  
Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

On the influence of different randomization and complex network analysis for differential evolution

2016 ◽  
Author(s):  
Roman Senkerik ◽  
Adam Viktorin ◽  
Michal Pluhacek ◽  
Jakub Janostik ◽  
Donald Davendra
Keyword(s):  
Network Analysis ◽  
Differential Evolution ◽  
Complex Network ◽  
Complex Network Analysis

Differential evolution based on the node degree of its complex network: Initial study

2016 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Differential Evolution ◽  
Complex Network ◽  
Initial Study ◽  
Node Degree

scholarly journals Local population analysis in the function of the protected area sustainable development

2021 ◽  
Vol 71 (3) ◽  
pp. 265-281
Author(s):  
Tamara Jojic-Glavonjic ◽  
Vlasta Kokotovic-Kanazir ◽  
Marija Ljakoska
Keyword(s):  
Protected Area ◽  
Population Analysis ◽  
Local Population ◽  
Age Groups ◽  
Population Data ◽  
Health Care Facilities ◽  
Social Infrastructure ◽  
Active Population ◽  
Key Factor ◽  
And Migration

The research focus of the paper is set on the socio-economic potential of a protected area, as a key factor and a prerequisite for its development. The spatial framework of the research includes five settlements in the vicinity of Special Nature Reserve ?Carska Bara? (Northern Serbia). For the purpose of this research, they are classified into two groups, based on their distance from the fundamental phenomenon. The demographic characteristics analyses of the study area include basic demographic determinants such as population structures and migration characteristics. Population data related to the change in the number of inhabitants and the types of the total population movement were collected and analyzed, and a comparative analysis of the aging index was performed as well. In order to better understand the condition of the economic structure, the economic activity, and the structure of the active population performing occupation were analyzed by activity sections. The current state of the social infrastructure (schools, primary health care facilities, pharmacies, post offices, sports, and recreation facilities) was also considered, as one of the qualities of life indicators of the local population. The obtained results indicate an unfavorable demographic picture of the analyzed areas. These are smaller population areas, predominantly inhabited by population of the old age groups. Although they are in protected areas which, in the context of tourism, are abounding in natural potentials, but without implementing significant steps and certain measures, no progress and improvement of the demographic condition can be expected.

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

LZW differential evolution for binary encoding

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