Seyyedabbasi, Amir2025-04-182025-04-182024Seyyedabbasi, A. (2024). A comprehensive survey: Evolutionary-based algorithms. Decision-Making Models, 77-84.978-044316147-6, 978-044316148-3http://dx.10.1016/B978-0-443-16147-6.00031-1https://hdl.handle.net/20.500.12713/6536Evolutionary algorithms (EAs) are optimization algorithms based on natural selection and evolution. The operators selected, reproduce, crossover, and mutation are used to evolve a population of candidate solutions iteratively. A variety of complex optimization problems in diverse domains have been successfully solved using EAs. An overview of genetic algorithms (GAs), differential evolution (DE), and genetic programming (GP) is presented in this chapter. It emphasizes the capabilities of EAs, including the ability to explore large problem spaces, handle nonlinear and multimodal search spaces, and accommodate a wide range of objectives and constraints. Although these algorithms are capable of handling complex and nonlinear search spaces, they also face challenges such as computational complexity and premature convergence. In order to enhance their performance, researchers are focusing on hybridization, parameter tuning, and parallelization. These algorithms will remain important tools in optimization and machine learning as computational resources increase, with promising future prospects in a wide range of fields. © 2024 Elsevier Inc. All rights reserved.eninfo:eu-repo/semantics/closedAccessEvolution Based AlgorithmEvolutionary AlgorithmsOptimization ProblemsBook Chapter2-s2.0-8520285741410.1016/B978-0-443-16147-6.00031-1