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 identified significant differences in the energy consumption of four widely used nature-inspired ...

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 ...

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 ...

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

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

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 ...