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

Numerous multi-objective optimization problems encounter with a number offitness functions to be simultaneously optimized of which their mutualpreferences are not inherently known. Suffering from the lack of underlyinggenerative models, the existing convex optimization approaches may fail toderive the Pareto optimal solution for those problems in complicated domainssuch as image enhancement. In order to obviate such shortcomings, theCompromising Evolution Method is proposed in this report to modify the SimpleGenetic Algorithm by utilizing the notion of compromise. The simulation resultsshow the power of the proposed method solving multi-objective optimizations ina case study of image error concealment.

Application of Compromising Evolution in Multi-objective Image Error Concealment