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Название: Evolutionary Algorithms in Engineering Design Optimization
Автор: David Greiner, Antonio Gaspar-Cunha, Daniel Hernandez-Sosa
Издательство: MDPI
Год: 2022
Страниц: 316
Язык: английский
Формат: pdf (true)
Размер: 29.8 MB
Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. The manuscripts cover a wide spectrum in terms of type of problems, methodologies and applications. Type of problems: single-objective and multi-objective optimization (among them, analysis of archiving strategies in evolutionary multi-objective algorithms, and preference directions in multi-objective optimization problems). Methods: genetic programming, genetic algorithms, particle swarm optimization, differential evolution, estimation of distribution algorithms, memetic algorithms, among others.
Автор: David Greiner, Antonio Gaspar-Cunha, Daniel Hernandez-Sosa
Издательство: MDPI
Год: 2022
Страниц: 316
Язык: английский
Формат: pdf (true)
Размер: 29.8 MB
Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. The manuscripts cover a wide spectrum in terms of type of problems, methodologies and applications. Type of problems: single-objective and multi-objective optimization (among them, analysis of archiving strategies in evolutionary multi-objective algorithms, and preference directions in multi-objective optimization problems). Methods: genetic programming, genetic algorithms, particle swarm optimization, differential evolution, estimation of distribution algorithms, memetic algorithms, among others.