From Darwinian Evolution to Swarm Computation and Gamesourcing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244899" target="_blank" >RIV/61989100:27240/19:10244899 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/19:10244899
Result on the web
<a href="https://www.taylorfrancis.com/books/e/9781315167084/chapters/10.1201/9781315167084-7" target="_blank" >https://www.taylorfrancis.com/books/e/9781315167084/chapters/10.1201/9781315167084-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1201/9781315167084" target="_blank" >10.1201/9781315167084</a>

Result language
angličtina
Original language name
From Darwinian Evolution to Swarm Computation and Gamesourcing
Original language description
This chapter discusses how to generate a network reflecting the behavior of selected evolutionary and swarm algorithms as much as possible to make a thorough analysis of relationships between individuals in the population. Such analysis is considered to be the starting point in the process of development of enhanced algorithms, where the results of the analysis are incorporated. The chapter investigates if there is a connection between an individual with the best fitness value and a node with the highest out-degree. Clustering coefficients of short-interval networks (SIN) generated on the basis of the differential evolution (DE) algorithms are very small; however, there are statistically significant differences between clustering coefficients of networks generated on the basis of the different DE algorithms. The principle of SINs creation as well as the principle of DE algorithms has caused very low density of networks.
Czech name
—
Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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