Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

Abstract: Evolutionary algorithms make countless random decisions during selection, mutation, and crossover operations. These random decisions require a steady stream of random numbers. We analyze the ...

If it feels like social platforms suddenly “get” you more than they used to, you’re not imagining it! In 2026, feeds aren’t only reacting to what you click anymore. They’re predicting what you ...

Individual sensor systems have limitations in the complex task of classifying shredded tobacco. This study aims to overcome these limitations by developing a novel evolutionary algorithm-based feature ...

When tools like ChatGPT first launched, they weren’t even connected to the internet. No real-time knowledge, no live queries, just pretrained data with a cutoff point. Two years later, Perplexity, a ...

As the world races to build artificial superintelligence, one maverick bioengineer is testing how much unprogrammed intelligence may already be lurking in our simplest algorithms to determine whether ...

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 ...

A new evolutionary technique from Japan-based AI lab Sakana AI enables developers to augment the capabilities of AI models without costly training and fine-tuning processes. The technique, called ...

Abstract: Multi-area optimal power flow (OPF) considering valve-point loading effects (VPL) is a large-scale constrained optimization problem with non-convexity and non-differentiability. Existing ...