Applied Evolutionary Algorithms for Engineers using Python 1st Edition by Leonardo Azevedo Scardua 9781000349801 1000349802

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ISBN 10: 1000349802
ISBN 13: 9781000349801 
Author: Leonardo Azevedo Scardu

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 

Applied Evolutionary Algorithms for Engineers using Python 1st Table of contents:

Section I: Introduction

1. Evolutionary Algorithms and Difficult Optimization Problems

1.1 What Makes an Optimization Problem Harder to Solve
1.2 Why Evolutionary Algorithms

2. Introduction to Optimization

2.1 What is Optimization
2.2 Solutions of an Optimization Problem
2.3 Maximization or Minimization
2.4 Basic Mathematical Formulation
2.5 Constraints and Feasible Regions
2.6 Local Solutions and Global Solutions
2.7 Multimodality
2.8 Multi-Objective Optimization
2.9 Combinatorial Optimization

3. Introduction to Evolutionary Algorithms

3.1 Representing Candidate Solutions

• 3.1.1 Discrete Representations
• 3.1.2 Integer Representation
• 3.1.3 Real-valued Representation
  3.2 Comparing Representations on a Benchmark Problem
  3.3 The Fitness Function
  3.4 Population
  3.5 Selecting Parents
• 3.5.1 Selection Probabilities
• 3.5.2 Sampling
• 3.5.3 Selection of Individuals
  3.6 Crossover (Recombination)
• 3.6.1 Recombination for Discrete Representations
• 3.6.2 Recombination for Real-valued Representations
  3.7 Mutation
• 3.7.1 Mutation for Binary Representations
• 3.7.2 Mutation for Real-valued Representations
• 3.7.3 Mutation for Integer Representations
  3.8 Elitism

Section II: Single-Objective Evolutionary Algorithms

4. Swarm Optimization

4.1 Ant Colony Optimization
4.2 Particle Swarm Optimization

5. Evolution Strategies

5.1 Recombination Operators
5.2 Mutation Operators
5.3 The (1 + 1) ES
5.4 The (μ + λ) ES
5.5 Natural Evolution Strategies
5.6 Covariance Matrix Adaptation Evolution Strategies

6. Genetic Algorithms

6.1 Real-Valued Genetic Algorithm
6.2 Binary Genetic Algorithm

7. Differential Evolution

Section III: Multi-Objective Evolutionary Algorithms

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

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