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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Evolutionary algorithms (EAs) have been applied successfully in many fields. However, EAs cannot find an optimal solution on many occasions because the balance between exploration and exploitation is lost in runs. So far, tricking the balance is an important research topic in the field of evolutionary computation. Elitism strategy is a typical scheme applied in selection for the above purpose and can be widely used in different EAs. In this paper, we propose elitism and distance strategy based on the elitism strategy. According to our strategy, elites are still kept in selection for reducing genetic drift. Meanwhile, the individual among candidates for selection having the longest distance to each elite is also kept for maintaining diversity. We carry out experiments based on not only a genetic algorithm for the traveling salesman problem but also two differential evolution algorithms, DE/rand/2/bin and CoBiDE. Experimental results show that adding our strategy in all generations can significantly improve solutions of the genetic algorithm for the traveling salesman problem. Moreover, calling our strategy at a low probability can significantly improve solutions of DE/rand/2/bin, while calling the strategy based on our proposed adaptive scheme can statistically improve solutions of CoBiDE, a state-of-the-art differential evolution algorithm.

Elitism and Distance Strategy for Selection of Evolutionary Algorithms