Evolutionary optimization methods for engineering, Part I, Genetic algorithms:
Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subjec...
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| Main Author: | |
|---|---|
| Format: | Electronic Video |
| Language: | English |
| Published: |
United States
IEEE
2011
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| Subjects: | |
| Online Access: | FHN01 TUM01 |
| Summary: | Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subject of this course which is divided into two parts. This course is Part I and covers the applications of Genetic Algorithms (GA). The underlying fundamental concepts of Genetic Algorithms are presented and then some representative examples are provided. Useful references are listed to allow for a more in-depth understanding of this topic |
| Item Description: | Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 13, 2020) |
| Physical Description: | 1 Online-Resource (1 Videodatei, 60 Minuten) |
| ISBN: | 9781612845050 |
Staff View
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| spelling | Rahmat-Samii, Yahya Verfasser aut Evolutionary optimization methods for engineering, Part I, Genetic algorithms Yahya Rahmat-Samii United States IEEE 2011 1 Online-Resource (1 Videodatei, 60 Minuten) tdi rdacontent c rdamedia cr rdacarrier Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 13, 2020) Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subject of this course which is divided into two parts. This course is Part I and covers the applications of Genetic Algorithms (GA). The underlying fundamental concepts of Genetic Algorithms are presented and then some representative examples are provided. Useful references are listed to allow for a more in-depth understanding of this topic Genetic algorithms Evolutionary computation (DE-588)4017102-4 Film gnd-content |
| spellingShingle | Rahmat-Samii, Yahya Evolutionary optimization methods for engineering, Part I, Genetic algorithms Genetic algorithms Evolutionary computation |
| subject_GND | (DE-588)4017102-4 |
| title | Evolutionary optimization methods for engineering, Part I, Genetic algorithms |
| title_auth | Evolutionary optimization methods for engineering, Part I, Genetic algorithms |
| title_exact_search | Evolutionary optimization methods for engineering, Part I, Genetic algorithms |
| title_exact_search_txtP | Evolutionary optimization methods for engineering, Part I, Genetic algorithms |
| title_full | Evolutionary optimization methods for engineering, Part I, Genetic algorithms Yahya Rahmat-Samii |
| title_fullStr | Evolutionary optimization methods for engineering, Part I, Genetic algorithms Yahya Rahmat-Samii |
| title_full_unstemmed | Evolutionary optimization methods for engineering, Part I, Genetic algorithms Yahya Rahmat-Samii |
| title_short | Evolutionary optimization methods for engineering, Part I, Genetic algorithms |
| title_sort | evolutionary optimization methods for engineering part i genetic algorithms |
| topic | Genetic algorithms Evolutionary computation |
| topic_facet | Genetic algorithms Evolutionary computation Film |
| work_keys_str_mv | AT rahmatsamiiyahya evolutionaryoptimizationmethodsforengineeringpartigeneticalgorithms |