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

Quality-Diversity (QD) optimisation is a new family of learning algorithmsthat aims at generating collections of diverse and high-performing solutions.Among those algorithms, the recently introduced Covariance Matrix AdaptationMAP-Elites (CMA-ME) algorithm proposes the concept of emitters, which uses apredefined heuristic to drive the algorithm's exploration. This algorithm wasshown to outperform MAP-Elites, a popular QD algorithm that has demonstratedpromising results in numerous applications. In this paper, we introduceMulti-Emitter MAP-Elites (ME-MAP-Elites), an algorithm that directly extendsCMA-ME and improves its quality, diversity and data efficiency. It leveragesthe diversity of a heterogeneous set of emitters, in which each emitter typeimproves the optimisation process in different ways. A bandit algorithmdynamically finds the best selection of emitters depending on the currentsituation. We evaluate the performance of ME-MAP-Elites on six tasks, rangingfrom standard optimisation problems (in 100 dimensions) to complex locomotiontasks in robotics. Our comparisons against CMA-ME and MAP-Elites show thatME-MAP-Elites is faster at providing collections of solutions that aresignificantly more diverse and higher performing. Moreover, in cases where nofruitful synergy can be found between the different emitters, ME-MAP-Elites isequivalent to the best of the compared algorithms.

Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters