| Description |
1 online resource (254 p.) |
| Contents |
Cover -- Title Page -- Copyright Page -- Preface -- Contents -- Glossary -- 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 |
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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 |
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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 |
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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 |
| Notes |
Description based upon print version of record |
| Form |
Electronic book
|
| ISBN |
9781000349740 |
|
1000349748 |
|
