A team led by Prof Frank Glorius from the Institute of Organic Chemistry at the University of Münster has developed an evolutionary algorithm that identifies the structures in a molecule that are ...

Researchers have introduced Hooded Seal optimization, a swarm-based evolutionary metaheuristic that outperformed or matched ...

In a new UCLA-led study, investigators shed light on the intricate processes underlying cancer evolution and define the optimal algorithms to analyze the genetic makeup of tumors. Understanding the ...

Population-based optimisation algorithms typically decide which candidate solutions survive using only their fitness at the current iteration, discarding all earlier performance information. This ...

Researchers have developed an exact fixed-parameter algorithm and a fast heuristic for deciding whether undirected ...

Evolutionary reinforcement learning is an exciting frontier in machine learning, combining the strengths of two distinct approaches: reinforcement learning and evolutionary computation. In ...

Research published in Nature Ecology & Evolution introduces a novel method for inferring DNA methylation patterns in non-skeletal tissues from ancient specimens, providing new insights into human ...

An international team led by the Clínic-IDIBAPS-UB along with the Institute of Cancer Research, London, has developed a new method based on DNA methylation to decipher the origin and evolution of ...

Evolution is a very slow process, due largely to the fact that nature doesn't "know" in advance which features of an animal will be beneficial. A new AI-based algorithm does know, however, allowing it ...

Imagine a very complex problem—supply chain optimization, for example—in which a computer generates millions of trial solutions completely at random and then ...