Computational scientists have developed algorithms inspired by natural evolution for at least
50 years. These algorithms solve optimization and design problems by building solutions …

Evolutionary computation is inspired by the mechanisms of biological evolution. With
algorithmic improvements and increasing computing resources, evolutionary computation …

In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

This paper capitalizes on the increasingly high relevance gained by data-intensive
technologies in the development of intelligent transportation system, which calls for the …

Evolutionary computation (EC), a collective name for a range of metaheuristic black-box
optimization algorithms, is one of the fastest-growing areas in computer science. Many …

The growing complexity of real-world problems has motivated computer scientists to search
for efficient problem-solving methods. Evolutionary computation and swarm intelligence …

Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …

Nature-Inspired Algorithms have been gaining much popularity in recent years due to the
fact that many real-world optimisation problems have become increasingly large, complex …

Benchmarking is a cornerstone in the analysis and development of computational methods,
especially in the field of evolutionary computation, where theoretical analysis of the …

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been
widely used to solve various scientific and engineering problems because of to their …