Fragments of DNA from long-extinct human relatives still circulate in modern genomes, and in some cases they do more than ...

Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer ...

Michelle Lee, PhD, unpacks how physical AI that integrate scientific reasoning with the wet lab will accelerate biological discovery.

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...

In A Nutshell Harvard researchers developed BrainIAC, a single AI model that can be adapted to analyze brain scans for ...

Abstract: This paper contains a comparison between a Genetic Algorithm (GA) and a Non-dominated Sorting Genetic Algorithm II (NSGA-II) on the Portfolio Optimisation Problem, based on the Modern ...

In experiments, researchers showed that the disease-spreading insects couldn’t resist the sweet smell of a fungus that infected and killed them. By Jason P. Dinh Watch your back, DEET. There’s a new ...

The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a ...

Abstract: The genetic algorithm is one of the most commonly used metaheuristics due to its adaptability to various types of problems. However, its implementation can be challenging because of the ...

Institute of Logistics Science and Engineering of Shanghai Maritime University, Pudong, China Introduction: This study addresses the joint scheduling optimization of continuous berths and quay cranes ...