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Название: Genetic Programming for Production Scheduling
Автор: Fangfang Zhang, Su Nguyen, Yi Mei
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2021
Страниц: 357
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Scheduling, i.e., the assignment of resources to tasks and their sequencing, is an important challenge in many areas, including manufacturing, health care, construction, and even when scheduling processes within a computer. Given its wide ranging importance, it is not surprising that scheduling is one of the oldest and most researched topics in Operational Research. Evolutionary learning applies evolutionary computation to address optimisation problems in Machine Learning. Evolutionary computation is a computational intelligence technique inspired by natural evolution based on population. Evolutionary computation consists of a family of algorithms. The success of evolutionary computation relies on the improvement of individuals generation by generation. There are two main categories in EC, which are evolutionary algorithms such as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, and swarm intelligence such as particle swarm optimisation and ant colony optimisation. Evolutionary algorithms, especially genetic programming, are the focus in this book.
Автор: Fangfang Zhang, Su Nguyen, Yi Mei
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2021
Страниц: 357
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Scheduling, i.e., the assignment of resources to tasks and their sequencing, is an important challenge in many areas, including manufacturing, health care, construction, and even when scheduling processes within a computer. Given its wide ranging importance, it is not surprising that scheduling is one of the oldest and most researched topics in Operational Research. Evolutionary learning applies evolutionary computation to address optimisation problems in Machine Learning. Evolutionary computation is a computational intelligence technique inspired by natural evolution based on population. Evolutionary computation consists of a family of algorithms. The success of evolutionary computation relies on the improvement of individuals generation by generation. There are two main categories in EC, which are evolutionary algorithms such as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, and swarm intelligence such as particle swarm optimisation and ant colony optimisation. Evolutionary algorithms, especially genetic programming, are the focus in this book.