English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A number of representation schemes have been presented for use withinlearning classifier systems, ranging from binary encodings to neural networks.This paper presents results from an investigation into using discrete and fuzzydynamical system representations within the XCSF learning classifier system. Inparticular, asynchronous random Boolean networks are used to represent thetraditional condition-action production system rules in the discrete case andasynchronous fuzzy logic networks in the continuous-valued case. It is shownpossible to use self-adaptive, open-ended evolution to design an ensemble ofsuch dynamical systems within XCSF to solve a number of well-known testproblems.

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system