A Novel Approach of Applying the Differential Evolution to Spatial Discrete Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099378" target="_blank" >RIV/61989100:27240/16:86099378 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/16:86099378

Result on the web

<a href="http://dx.doi.org/10.3233/978-1-61499-672-9-1555" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-672-9-1555</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-672-9-1555" target="_blank" >10.3233/978-1-61499-672-9-1555</a>

Result language

angličtina

Original language name

A Novel Approach of Applying the Differential Evolution to Spatial Discrete Data

Original language description

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. (C) 2016 Vojtěch Uher et al.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

—

Project

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Frontiers in Artificial Intelligence and Applications. Volume 285

ISBN

978-1-61499-671-2

ISSN

0922-6389

e-ISSN

—

Number of pages

6

Pages from-to

1-6

Publisher name

IOS Press

Place of publication

Amsterodam

Event location

Haag

Event date

Aug 29, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000385793700182