Chapter 7 - A comprehensive survey: Evolutionary-based algorithms

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@InCollection{SEYYEDABBASI:2024:DM,

• author = "Amir Seyyedabbasi",
• title = "Chapter 7 - A comprehensive survey: Evolutionary-based algorithms",
• booktitle = "Decision-Making Models",
• publisher = "Academic Press",
• year = "2024",
• editor = "Tofigh Allahviranloo and Witold Pedrycz and Amir Seyyedabbasi",
• series = "Uncertainty, Computational Techniques, and Decision Intelligence",
• pages = "77--84",
• keywords = "genetic algorithms, genetic programming, Evolution based algorithm, Evolutionary algorithms, Optimization problems",
• isbn13 = "978-0-443-16147-6",
• URL = "https://www.sciencedirect.com/science/article/pii/B9780443161476000311",
• DOI = "doi:10.1016/B978-0-443-16147-6.00031-1",
• abstract = "Evolutionary algorithms (EAs) are optimisation 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 optimisation 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 optimisation and machine learning as computational resources increase, with promising future prospects in a wide range of fields",

}

Genetic Programming entries for Amir Seyyedabbasi

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