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

In this research, we investigate the possibility of applying a searchstrategy to genetic algorithms to explore the entire genetic tree structure.Several methods aid in performing tree searches; however, simpler algorithmssuch as breadth-first, depth-first, and iterative techniques arecomputation-heavy and often result in a long execution time. Adversarialtechniques are often the preferred mechanism when performing a probabilisticsearch, yielding optimal results more quickly. The problem we are trying totackle in this paper is the optimization of neural networks using geneticalgorithms. Genetic algorithms (GA) form a tree of possible states and providea mechanism for rewards via the fitness function. Monte Carlo Tree Search(MCTS) has proven to be an effective tree search strategy given states andrewards; therefore, we will combine these approaches to optimally search forthe best result generated with genetic algorithms.

MCTS guided Genetic Algorithm for optimization of neural network weights