Metaheuristic Evolutionary Algorithms: Types, Applications, Future Directions, and Challenges

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@InProceedings{Kavita:2023:CONIT,

• author = "Shelke Kavita and Shinde {S. K.}",
• booktitle = "2023 3rd International Conference on Intelligent Technologies (CONIT)",
• title = "Metaheuristic Evolutionary Algorithms: Types, Applications, Future Directions, and Challenges",
• year = "2023",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Evolution (biology), Metaheuristics, Sociology, Optimisation methods, Finance, Evolutionary computation, Optimisation, Evolutionary Algorithms, EAs, metaheuristic optimisation, population",
• DOI = "doi:10.1109/CONIT59222.2023.10205592",
• abstract = "Metaheuristic optimisation methods are widely used to solve complex optimisation problems in various fields such as engineering, finance, and logistics. Evolutionary Algorithms (EAs) are a family of metaheuristic optimisation algorithms that are inspired by biological evolution and natural selection. EAs mimic the process of natural selection by maintaining a population of candidate solutions and applying genetic operators such as mutation, crossover, and selection to generate new solutions over multiple generations. In this review, we will focus on six popular types of EAs: Genetic Algorithms (GA), Evolution Strategies (ES), Genetic Programming (GP), Differential Evolution (DE), Estimation of Distribution Algorithms (EDA), and Cultural Algorithms (CA). The study also provides insights into the selection of appropriate metaheuristic optimisation methods for solving specific optimisation problems.",
• notes = "Also known as \cite{10205592}",

}

Genetic Programming entries for Shelke Kavita Shinde S K

Citations