Leonardo Azevedo Scardua

Applied Evolutionary Algorithms for Engineers using Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems. The presentation of the theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems. Cases of successful application of evolutionary algorithms to real-world like optimization problems are presented, together with source code that allows the reader to gain insight into the idiosyncrasies of the practical application of evolutionary algorithms.

Key Features

• Includes detailed descriptions of evolutionary algorithm paradigms
• Provides didactic implementations of the algorithms in Python, a programming language that has been widely adopted by the AI community
• Discusses the application of evolutionary algorithms to real-world optimization problems
• Presents successful cases of the application of evolutionary algorithms to complex optimization problems, with auxiliary source code.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 225
• Erscheinungstermin: 14. Juni 2021
• Englisch
• ISBN-13: 9781000349740
• Artikelnr.: 61314702

Autorenporträt

Leonardo Azevedo Scardua received the B.S.E.E. degree in 1989 and the M.Sc. degree in 1996, both from the Federal University of Espírito Santo, Brazil, and the D.Sc. degree from the University of São Paulo Brazil, in 2015. He has extensive engineering experience with software systems for mission-critical applications, mainly in the railway industry. He is now with the Control Engineering Department at the Federal Institute of Technology of Espírito Santo, Brazil. His current research interests include evolutionary computation applied to control of dynamic systems with continuous action spaces and nonlinear state estimation.

Inhaltsangabe

Preface. SECTION I: INTRODUCTION. Evolutionary Algorithms and Difficult
Optimization Problems. Introduction to Optimization. Introduction to
Evolutionary Algorithms. SECTION II: SINGLE-OBJECTIVE EVOLUTIONARY
ALGORITHMS. Swarm Optimization. Evolution Strategies. Genetic Algorithms.
Differential Evolution. SECTION III: MULTI-OBJECTIVE EVOLUTIONARY
ALGORITHMS. Non-Dominated Sorted Genetic Algorithm II. Multiobjective
Evolutionary Algorithm Based on Decomposition. SECTION IV: APPLYING
EVOLUTIONARY ALGORITHMS. Solving Optimization Problems with Evolutionary
Algorithms. Assessing the Performance of Evolutionary Algorithms. Case
Study - Optimal Design of a Gear Train System. Case Study - Teaching a
Legged Robot How to Walk. References.

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Preface. SECTION I: INTRODUCTION. Evolutionary Algorithms and Difficult Optimization Problems. Introduction to Optimization. Introduction to Evolutionary Algorithms. SECTION II: SINGLE-OBJECTIVE EVOLUTIONARY ALGORITHMS. Swarm Optimization. Evolution Strategies. Genetic Algorithms. Differential Evolution. SECTION III: MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS. Non-Dominated Sorted Genetic Algorithm II. Multiobjective Evolutionary Algorithm Based on Decomposition. SECTION IV: APPLYING EVOLUTIONARY ALGORITHMS. Solving Optimization Problems with Evolutionary Algorithms. Assessing the Performance of Evolutionary Algorithms. Case Study - Optimal Design of a Gear Train System. Case Study - Teaching a Legged Robot How to Walk. References.