Applied Evolutionary Algorithms for Engineers Using Python
By Leonardo Azevedo Scardua
"Applied Evolutionary Algorithms for Engineers Using Python" is published by Taylor & Francis Group in 2021 - Boca Raton, it has 225 pages and the language of the book is English.
“Applied Evolutionary Algorithms for Engineers Using Python” Metadata:
- Title: ➤ Applied Evolutionary Algorithms for Engineers Using Python
- Author: Leonardo Azevedo Scardua
- Language: English
- Number of Pages: 225
- Publisher: Taylor & Francis Group
- Publish Date: 2021
- Publish Location: Boca Raton
“Applied Evolutionary Algorithms for Engineers Using Python” Subjects and Themes:
- Subjects: ➤ Evolutionary programming (Computer science) - Evolutionary computation - Genetic algorithms - Python (Computer program language) - Programmation évolutive - Réseaux neuronaux à structure évolutive - Algorithmes génétiques - Python (Langage de programmation) - COMPUTERS / Programming Languages / General - MATHEMATICS / Arithmetic - COMPUTERS / Programming Languages / Python
Edition Identifiers:
- The Open Library ID: OL33824249M - OL25265424W
- ISBN-13: 9781000349771 - 9781000349801 - 9781000349740 - 9780429298028
- All ISBNs: 9781000349771 - 9781000349801 - 9781000349740 - 9780429298028
AI-generated Review of “Applied Evolutionary Algorithms for Engineers Using Python”:
"Applied Evolutionary Algorithms for Engineers Using Python" Description:
Open Data:
8. Non-Dominated Sorted Genetic Algorithm II -- 9. Multiobjective Evolutionary Algorithm Based on Decomposition -- Section IV: Applying Evolutionary Algorithms -- 10. Solving Optimization Problems with Evolutionary Algorithms -- 10.1 Benchmark Problems -- 10.1.1 Single-Objective -- 10.1.2 Multi-Objective -- 10.1.3 Noisy -- 10.2 Dealing with Constraints -- 10.3 Dealing with Costly Objective Functions -- 10.4 Dealing with Noise -- 10.5 Evolutionary Multi-Objective Optimization -- 10.6 Some Auxiliary Functions -- 11. Assessing the Performance of Evolutionary Algorithms -- 11.1 A Cautionary Note -- 11.2 Performance Metric -- 11.3 Confidence Intervals -- 11.4 Assessing the Performance of Single-Objective Evolutionary Algorithms -- 11.5 Assessing the Performance of Multi-Objective Evolutionary Algorithms -- 11.6 Benchmark Functions -- 12. Case Study: Optimal Design of a Gear Train System -- 13. Case Study: Teaching a Legged Robot How to Walk -- References -- Index
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