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 ...

Matthew is a journalist in the news department at GameRant. He holds a Bachelor's degree in journalism from Kent State University and has been an avid gamer since 1985. Matthew formerly served as a ...

1 School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong, China. 2 Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of ...

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: In combinatorial optimization problems, traditional optimization algorithms are often difficult to efficiently find the global optimal solution. This paper studies the application of genetic ...

ABSTRACT: Ahead of the Internet of Things and the emergence of big data, the interest of research is today focused on radio access and the process of optimizing it or increasing its capacity and ...

This report explores the use of genetic algorithms for optimizing financial quantitative trading strategies. Genetic algorithms are a type of evolutionary computation method that mimics the natural ...

Abstract: Evolutionary Algorithms (EAs) are the solution approaches for solving optimization problems using the classical generate-and-test method. These algorithms are widely used by the ...

In recent years, due to rapid fossil fuel depletion (Peng et al., 2020), booming global energy demand (Shangguan et al., 2020a), and a series of severe eco-environmental problems (Yang et al., 2015), ...