Table of Contents
Preface
Part 1: The Basics of Genetic Algorithms
1
An Introduction to Genetic Algorithms
What are genetic algorithms?
Darwinian evolution
The genetic algorithms analogy
The theory behind genetic algorithms
The schema theorem
Differences from traditional algorithms
Population-based
Genetic representation
Fitness function
Probabilistic behavior
Advantages of genetic algorithms
Global optimization
Handling complex problems
Handling a lack of mathematical representation
Resilience to noise
Parallelism
Continuous learning
Limitations of genetic algorithms
Special definitions
Hyperparameter tuning
Computationally intensive
Premature convergence
No guaranteed solution
Use cases for genetic algorithms
Summary
Further reading
2
Understanding ...
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