A comprehensive survey: Evolutionary-based algorithms

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dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023
dc.contributor.authorSeyyedabbasi, Amir
dc.date.accessioned2025-04-18T08:16:04Z
dc.date.available2025-04-18T08:16:04Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractEvolutionary 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.
dc.identifier.citationSeyyedabbasi, A. (2024). A comprehensive survey: Evolutionary-based algorithms. Decision-Making Models, 77-84.
dc.identifier.doi10.1016/B978-0-443-16147-6.00031-1
dc.identifier.isbn978-044316147-6, 978-044316148-3
dc.identifier.scopus2-s2.0-85202857414
dc.identifier.urihttp://dx.10.1016/B978-0-443-16147-6.00031-1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6536
dc.indekslendigikaynakScopus
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDecision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEvolution Based Algorithm
dc.subjectEvolutionary Algorithms
dc.subjectOptimization Problems
dc.titleA comprehensive survey: Evolutionary-based algorithms
dc.typeBook Chapter

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