AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

This paper studies the problem of finding graphs that maximize the number of edges, while avoiding short cycles. It formulates graph generation as a reinforcement learning task, and compares methods like AlphaZero and tabu search. A key finding is that using a curriculum - building larger graphs from good smaller graphs - significantly improves performance. The work makes progress on an open problem in extremal graph theory.

Adam Zsolt Wagner

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment

Petar Veličković on LinkedIn: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

Comparison of Taboo Search Methods for Atomic Cluster Global Optimization with a Basin-Hopping Algorithm

Anian Ruoss (@anianruoss) / X

The shape of AGI: Cartoons and back of envelope — LessWrong

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

Adam Zsolt Wagner

Juan (@jeandut14000) / X

Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment

A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment