This book meant for students, scientists and engineers to help in the application of evolutionary algorithms to practical optimization problems. The presentation of theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems.
This book meant for students, scientists and engineers to help in the application of evolutionary algorithms to practical optimization problems. The presentation of theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems.
Leonardo Azevedo Scardua received the D.Sc. degree in electrical engineering from the University of São Paulo, Brazil, in 2015. He has extensive engineering experience with mission-critical applications in the railway industry, having applied artificial intelligence and optimization algorithms in the development of software systems that control train traffic in many railways. He is now with the Control Engineering Department at the Federal Institute of Technology of Espírito Santo, Brazil.
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.
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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