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 ...

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 ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability 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 ...

See /GLS/README.md for detailed documentation of this innovation. Population size: 200 Maximum generations: 300 Random mating probability (RMP): 0.4 Mutation rate: 0. ...

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry ...

Large language models (LLMs) leverage unsupervised learning to capture statistical patterns within vast amounts of text data. At the core of these models lies the Transformer architecture, which ...

ABSTRACT: Supply chain networks, which integrate nodes such as suppliers, manufacturers, and retailers to achieve efficient coordination and allocation of resources, serve as a critical component in ...

Abstract: The use of evolutionary algorithms (EAs) for the automated design of programs, electronic circuits, neural networks, and other computational structures has become a fruitful approach in the ...