Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...
Combinatorial optimization problems are encountered often in various real-world applications, including logistics, scheduling, and network design ...
The Sports Analytics Research Group employs quantitative analysis to give teams the hard numbers they need to perform better ...
In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...
A team of researchers at the University of Warwick and Monash University has solved a puzzle that has stumped drug developers ...
Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.
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Honeywell-backed Quantinuum just file...
Quantinuum Inc., the trapped-ion quantum computing company majority-owned by Honeywell, filed a Form S-1 registration statement with the U.S. Securities and Exchange Commission on May 8, 2026, firing ...
Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization
In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
Abstract: Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, ...
KAWASAKI, Japan--(BUSINESS WIRE)--Toshiba Corporation has developed a breakthrough algorithm that dramatically boosts the performance of the Simulated Bifurcation Machine (SBM), its proprietary ...
In an impressive feat, Japanese startup Sakana AI’s coding agent ALE-Agent recently secured first place in the AtCoder Heuristic Contest (AHC058), a complex coding competition that involves ...
Abstract: This paper defines a new combinatorial optimization problem, namely General Combinatorial Optimization Problem (GCOP), whose decision variables are a set of parametric algorithmic components ...
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