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

Exploiting knowledge about the structure of a problem can greatly benefit theefficiency and scalability of an Evolutionary Algorithm (EA). Model-Based EAs(MBEAs) are capable of doing this by explicitly modeling the problem structure.The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is among thestate-of-the-art of MBEAs due to its use of a linkage model and the optimalmixing variation operator. Especially in a Gray-Box Optimization (GBO) settingthat allows for partial evaluations, i.e., the relatively efficient evaluationof a partial modification of a solution, GOMEA is known to excel. Such GBOsettings are known to exist in various real-world applications to which GOMEAhas successfully been applied. In this work, we introduce the GOMEA library,making existing GOMEA code in C++ accessible through Python, which serves as acentralized way of maintaining and distributing code of GOMEA for variousoptimization domains. Moreover, it allows for the straightforward definition ofBBO as well as GBO fitness functions within Python, which are called from theC++ optimization code for each required (partial) evaluation. We describe thestructure of the GOMEA library and how it can be used, and we show itsperformance in both GBO and Black-Box Optimization (BBO).

A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA