Description

Evolutionary algorithms (EAs) are powerful tools in computational intelligence for addressing challenges in mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks. MANETs are decentralized networks of mobile nodes forming temporary connections without a fixed infrastructure, relying solely on wireless links. Energy efficiency is critical in MANETs, prompting the development of genetic algorithm-based routing protocols over the past decade. This book explores how EAs, which include heuristic and population-based collective learning methods, can optimize security and communication reliability in mobile networks. EAs adaptively optimize solutions through iterations, making them robust and suitable for dynamic environments like MANETs. The research presented synthesizes advancements in applying evolutionary methods to tackle the complexities of modern mobile and hybrid networks, providing insights for researchers and practitioners alike.